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Background: The detection of subclinical margin in lentigo maligna/lentigo maligna melanoma (LM/LMM) can be challenging for the dermatologist. Reflectance confocal microscopy (RCM) enables to observe in vivo atypical melanocytes beyond the clinical margins. The aim of this study is to establish which of these methods (clinical examination and dermoscopy versus "Paper tape – RCM") is more precise to define the lesion margin and to reduce the number of re‐intervention and overtreatments in cosmetically sensitive areas. Methods: Fifty‐seven cases of LM/LMM were analyzed during 2016–2022. Pre‐surgical mapping procedures in 32 lesions were effectuated with dermatoscopy. Furthermore, pre‐surgical mapping procedures in 25 lesions were effectuated with RCM and paper tape. Results: RCM method's accuracy to detect subclinical margins was 92.0%. In 24 of 25 cases, the lesions were excised completely during the first intervention. In 20 of 32 cases analyzed with dermoscopy, a second surgical intervention was effectuated. Conclusion: The RCM paper method allows us to delineate subclinical margin more accurately and reduce overtreatment, especially in sensitive areas, such as the face and neck. [ABSTRACT FROM AUTHOR]

Background: Line‐field confocal optical coherence tomography (LC‐OCT) is an emerging diagnostic tool with imaging depth reaching ~400 μm and a novel three‐dimensional (3D) cube providing cellular resolution. As far as we are aware, there are only a limited number of papers that have reported diagnostic criteria for melanocytic lesions using this technique, and none of them have been multicentric. Objectives: Our aim was to establish the diagnostic criteria for melanocytic lesions using LC‐OCT and identify the most significant architectural and cytologic features associated with malignancy. Methods: A retrospective evaluation of 80 consecutive melanocytic lesions from a prospective multicentric data set spanning three European centres was conducted. We excluded facial, acral and mucosal lesions from the study. Dermoscopic and LC‐OCT images were evaluated by a consensus of four observers. Multivariate logistic regression with backward elimination was employed. Results: The main melanoma diagnostic criteria include detecting >10 pagetoid cells in 3D acquisition, irregular 3D epidermal architecture, disrupted dermoepidermal junction (DEJ) and clefting. Significant risk factors were irregular 3D epidermal architecture, >10 pagetoid cells, dendritic cells at DEJ without underlying inflammation. Novel malignancy criteria in vertical view were DEJ disruption and clefting around atypical melanocyte nests. Exclusive melanoma features were epidermal nests, epidermal consumption, dense dermal nests with atypia. Protective features in the absence of any malignancy indicators were DEJ ring pattern, cobblestone, elongated rete ridges (vertical), well‐defined DEJ and wave pattern (vertical). Conclusions: A series of diagnostic criteria for the identification of melanocytic lesions with LC‐OCT have been established. Validation of these criteria in clinical practice through future studies is essential to further establish their utility. [ABSTRACT FROM AUTHOR]

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research. [ABSTRACT FROM AUTHOR]

A dermatologist-like automatic classification system is developed in this paper to recognize nine different classes of pigmented skin lesions (PSLs), using a separable vision transformer (SVT) technique to assist clinical experts in early skin cancer detection. In the past, researchers have developed a few systems to recognize nine classes of PSLs. However, they often require enormous computations to achieve high performance, which is burdensome to deploy on resource-constrained devices. In this paper, a new approach to designing SVT architecture is developed based on SqueezeNet and depthwise separable CNN models. The primary goal is to find a deep learning architecture with few parameters that has comparable accuracy to state-of-the-art (SOTA) architectures. This paper modifies the SqueezeNet design for improved runtime performance by utilizing depthwise separable convolutions rather than simple conventional units. To develop this Assist-Dermo system, a data augmentation technique is applied to control the PSL imbalance problem. Next, a pre-processing step is integrated to select the most dominant region and then enhance the lesion patterns in a perceptual-oriented color space. Afterwards, the Assist-Dermo system is designed to improve efficacy and performance with several layers and multiple filter sizes but fewer filters and parameters. For the training and evaluation of Assist-Dermo models, a set of PSL images is collected from different online data sources such as Ph2, ISBI-2017, HAM10000, and ISIC to recognize nine classes of PSLs. On the chosen dataset, it achieves an accuracy (ACC) of 95.6%, a sensitivity (SE) of 96.7%, a specificity (SP) of 95%, and an area under the curve (AUC) of 0.95. The experimental results show that the suggested Assist-Dermo technique outperformed SOTA algorithms when recognizing nine classes of PSLs. The Assist-Dermo system performed better than other competitive systems and can support dermatologists in the diagnosis of a wide variety of PSLs through dermoscopy. The Assist-Dermo model code is freely available on GitHub for the scientific community. [ABSTRACT FROM AUTHOR]

Background: The umbilicus is a fibrous remnant located in the centre of the abdomen. Various entities may be encountered in this special anatomical location; however, little is known about their dermoscopic presentation. The aim of this study was to provide a comprehensive summary of existing evidence on dermoscopic features of umbilical lesions. Methods: Studies assessing dermoscopic images of umbilical lesions were included in this study. No age, ethnicity or skin phototype restrictions were applied. Papers assessing lesions outside of the umbilical area, lacking dermoscopic images and/or dermoscopic description and not related to the topic were excluded. Embase, Medline and Cochrane Library were searched from inception to the end of May 2023. The Joanna Briggs Institute critical appraisal tools were used to evaluate the risk of bias of the selected studies. The quality and the level of evidence of included studies were assessed according to the Oxford 2011 Levels of Evidence. Thirty-four studies reporting a total of 39 lesions met the inclusion criteria and were included in qualitative analysis. Results: A qualitative synthesis of the following entities was performed: melanoma, nevi, basal cell carcinoma, fibroepithelioma of Pinkus, Sister Mary Joseph nodule, mycosis fungoides, dermatofibroma, endometriosis, epidermal cyst, granuloma, intravascular papillary endothelial hyperplasia, lichen planus, omphalolith, seborrheic keratosis, and syringoma. Conclusions: Dermoscopy is a non-invasive technique that may be useful in the differential diagnosis of umbilical lesions. The main limitations of this study were lack of a high level of evidence in the studies and the lack of uniformity in applied dermoscopic terminology between included studies. [ABSTRACT FROM AUTHOR]

Segmentation of skin lesions is a significant and demanding task in dermoscopy images. This paper proposes a new skin cancer recognition scheme, with: "Pre-processing, Segmentation, Feature extraction, Optimal Feature Selection and Classification". Here, pre-processing is done with certain processes. The pre-processed images are segmented via the "Otsu Thresholding model". The third phase is feature extraction, where Deviation Relevance-based "Local Binary Pattern (DRLBP), Gray-Level Co-Occurrence Matrix (GLCM) features and Gray Level Run-Length Matrix (GLRM) features" are extracted. From these extracted features, the optimal features are chosen via Particle Updated WOA (PU-WOA) model. Subsequently, classification occurs via Optimized DCNN and NN to classify the skin lesion. To make the classification more precise, the DCNN is optimized by the introduced algorithm. The result has shown a higher accuracy of 0.998737, when compared with other extant models like IPSO, IWOA, PSO+CNN, WOA+CNN and CNN schemes. [ABSTRACT FROM AUTHOR]

Introduction: Medical conditions can cause the skin to become hypopigmented or depigmented, mainly due to decreased production of melanin. Hypomelanosis is mainly benign and rarely malignant. Depigmentation refers to a complete lack of melanin, with the most common cause being vitiligo. Differentiating between these conditions can be difficult. Diagnosis of the condition is primarily based on the patient's detailed history, clinical signs and symptoms, accurate evaluation, and dermoscopy. Repigmentation can occur following early diagnosis and appropriate management. Aim: To highlight diagnostic findings of various cutaneous hypopigmented macular lesions and patches. Materials and Methods: PubMed and Google Scholar databases were searched using a mix of terms, including "cutaneous disorders", "dermoscopy", "skin biopsy", and "hypopigmented disorders" for this scoping review, which followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. Boolean operators "AND" and "OR" were used between the keywords. The inclusion criteria consisted of articles with full text availability, articles describing various cutaneous disorders with characteristic morphology, diagnosis, types and subtypes, conditions associated with systemic diseases, histological examination findings, and prognosis of the condition, peer-reviewed papers with a comprehensive diagnosis of cutaneous hypopigmented diseases, histological biopsies. Randomised Controlled Trials (RCTs), review articles, case reports, and articles in the English language were included in this review. Results: Based on the selection criteria, a total of 12 studies were included in the review, describing various cutaneous disorders with characteristic morphology, diagnosis, types and subtypes, conditions associated with systemic diseases, histological examination findings, and prognosis of the condition. Conclusion: Knowledge regarding various outcomes from the studies related to diagnostic findings in various cutaneous hypopigmented disorders is essential for dermatologists for awareness, appropriate examination, and adequate treatment. [ABSTRACT FROM AUTHOR]

Background: Agmination and/or satellitosis in pigmented blue lesions is a phenomenon rarely mentioned in the literature and not well known. This phenomenon can be expressed by several benign and malignant pigmented blue lesions, such as blue nevi, Spitz nevi, melanocytoma and melanoma. On this spectrum, dermoscopy, reflectance confocal microscopy (RCM) and dynamic Optical coherence tomography (D-OCT) represent non-invasive imaging technologies, which may help clinicians in the diagnosis of melanoma and non-melanoma skin cancers in daily clinical practice. Methods: Currently, in the literature there is a lack of new data about agminated blue lesions and blues lesions with satellitosis, as well as the lack of a recent and updated review of the literature about this topic. Therefore, considering that clinicians must be confident with the diagnosis of these rare skin lesions, we decided to carry out this work. Results: In this paper, four new cases of agminated pigmented cutaneous lesions were described. Moreover, a review of the current literature on this topic was performed. Conclusions: A clinical–pathological correlation is often needed to reach a correct diagnosis; currently, dermoscopy and non-invasive diagnostic techniques, such as reflectance confocal microscopy and optical coherence tomography, due to the depth of these skin lesions in the dermis, can only make a partial and limited contribution. [ABSTRACT FROM AUTHOR]

Dermatoscopy is a non-invasive and cost-efficient imaging technique augmenting clinical examination in neoplastic and non-neoplastic dermatoses. Recently, novel dermatoscopic techniques based on principles of reflectance/absorption and excited fluorescence have been developed. However, comprehensive data on their applications are sparse, and terminology is inconsistent. In this systematic review, we addressed the principles of ultraviolet (UV) imaging and proposed categorization based on spectral characteristics and signal acquisition, as well as discussed documented and potential clinical applications, safety measures during examination, and limitations associated with reflectance and fluorescence dermatoscopy. A literature search was conducted in the PubMed medical database until 2 December 2023 according to PRISMA guidelines, and 28 papers fit the scope of this review, whereas additional relevant articles were included to provide broader context regarding the chosen terminology, chromophores described, safety of sub-UV/UV, and regulations for light-emitting devices. UV and sub-UV dermatoscopy, categorized into different methods on the basis of the emitted wavelength and signal acquisition process (reflectance versus fluorescence), augment conventional dermatoscopy by optimizing safety margins in melanoma, facilitating early detection of tumor recurrence, and enhancing visualization in non-neoplastic conditions, including pigmentation disorders, intertrigo, papulo-desquamative dermatoses, and beyond. The review highlights the limitations of these techniques, including difficulty in differentiating melanin from hemoglobin, challenges in evaluating uneven surfaces, and artifacts. Although UV dermatoscopy complements conventional dermatoscopy, clinicians should be aware of their peculiarities, artifacts, limitations, and safety concerns to optimize their diagnostic accuracy and ensure patient's safety. [ABSTRACT FROM AUTHOR]

The segmentation of dermoscopic‐based skin lesion images is considered to be challenging owing to various factors. Some of the most tangible reasons include poor contrast near the affected skin lesion, the fuzzy and unpredictable lesion limits, the presence of variations in noise, and capturing images under different conditions. This paper aims to develop an efficient segmentation model for dermoscopic images of different skin lesions based on deep learning. This paper proposes the 11‐layer convolutional deep neural network with two segmentation models trained from start to finish and do not depend on any previous information about the data. The viability, efficiency, and speculation ability of the models are evaluated on the ISIC2018 database. The proposed model achieves 0.903 accuracy and 0.820 Jaccard index in the segmentation of skin lesions. The model shows better performance compared to other image segmentation techniques from the leaderboards of ISIC2018 using deep learning. [ABSTRACT FROM AUTHOR]

The advent of deep learning techniques has ignited interest in medical image processing. The proposed work in this paper suggests one of the edge technologies in deep learning, which is recommended, based on a Radiomics feature extraction model for the effective detection of Kaposi sarcoma, a vascular skin lesion expression that indicates the most prevalent cancer in AIDS patients. This work investigates the role and impact of medical image fusion on deep feature learning based on ensemble learning in the medical domain. The model is crafted wherein the pre-built ResNet50 (Residual network) and Visual Geometry Group (VGG16) are fine-tuned and an ensemble learning approach is applied. The pre-defined CNN was incrementally regulated to determine the appropriate standards for classification efficiency improvements. Our findings show that layer-by-layer fine-tuning can improve the performance of middle and deep layers. This work would serve the purpose of masking and classification of skin lesion images, primarily sarcoma using an ensemble approach. Our proposed assisted framework could be deployed in assisting radiologists by classifying Kaposi sarcoma as well as other related skin lesion diseases, based on the positive classification findings. [ABSTRACT FROM AUTHOR]

In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency–based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence. [ABSTRACT FROM AUTHOR]

Introduction: Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. Method: This research uses a hybrid deep learning model that combines two cutting‐edge approaches: Mask Region‐based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. Results: The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state‐of‐the‐art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. Conclusion: In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN) and implemented on three publicly available benchmark datasets (ISIC 2020, HAM10000, and DermIS). The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17% accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The accuracies of ResNet-152, Vgg-19, MobileNet, and Vgg-16, EfficientNet-B0, and Inception-V3 are 89.68%, 92.51%, 91.46%, 89.12%, 89.46%, and 91.82%, respectively. The results showed that the proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most dangerous type of skin cancer worldwide. Early detection of melanoma is crucial for better outcomes, but this often can be challenging. This research explores the use of artificial intelligence (AI) techniques combined with non-invasive imaging methods to improve melanoma detection. The authors aim to evaluate the current state of AI-based techniques using tools including dermoscopy, optical coherence tomography (OCT), and reflectance confocal microscopy (RCM). The findings demonstrate that several AI algorithms perform as well as or better than dermatologists in detecting melanoma, particularly in the analysis of dermoscopy images. This research highlights the potential of AI to enhance diagnostic accuracy, leading to improved patient outcomes. Further studies are needed to address limitations and ensure the reliability and effectiveness of AI-based techniques. Background: Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. Objective: The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. Methods: A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. Results: We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Conclusions: Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability. [ABSTRACT FROM AUTHOR]

Folliculotropic mycosis fungoides (FMF) represents 10% of all mycosis fungoides cases and even though supraciliary lesions and alopecia are characteristic, there are few published papers documenting trichoscopic findings in these patients. We report the case of a 50-year-old man who presented to our department with FMF stage IB. Clinical findings included disseminated erythematous patches and plaques with a fine white-grayish scale, madarosis, and multifocal patchy alopecia of the scalp. Trichoscopy revealed a decreased number of pilosebaceous units, dilated follicular openings, black dots, vellus, and dystrophic hairs. Examination of the scalp presented widespread white scaling and areas with dotted and spermatozoa-like vessels. A revision of the literature showed that dilated follicular openings, black dots, and scale were less frequent findings in FMF, and dystrophic hairs were more common in advanced FMF. In the future, trichoscopic evaluation might guide differential diagnosis and define the threshold to biopsy lesions to identify early disease. [ABSTRACT FROM AUTHOR]

Skin cancer is the most deadliest forms of all other cancers combined; In this paper various pre and post-treatments are presents for improving automated melanomas diagnosis of dermoscopy images. At first pre-processing have done to exclude unwanted parts, a triple A-segmentation proposes to extract lesion according to their histogram patterns. Lastly, suggest appending process with testing many factors for superior detection decision. This paper argues different detection rules: first system used fuzzy rules based on a different features, a second test have been done by modeled local colours with bag-of-features classifier. Then add lesion shape on two previous systems as their global form in the first one, while distributed it and appending with local colour patches in the second system. For each case, different features; various colour models, and many other parameters are examines to decide which settings are more discriminative. Evaluation performance of each method has carried out on (ISIC2019 Challenge) dermoscopic database. The higher classification accuracy results 98.26% with prove its specific parameters that achieved by appending global asymmetric feature to the colour patches. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the deadliest cancers, and it has been widely developed worldwide since the last decade. Malignant melanoma is currently the most deadly skin cancer. If malignant melanoma is diagnosed at an early stage, the probability of patients being cured will be greatly improved. At present, most existing skin lesion image classification methods only use deep learning. However, the multi‐modal features of skin lesions in the medical domain are not well utilized and integrated. To reduce the classification error of the skin lesion images caused by the complexity and subjectivity of visual interpretation, a malignant melanoma dermoscopy image classification method based on multi‐modal medical features is proposed in this paper which is inspired by the fuzzy decision‐making process of doctors. It can reduce the subjective difference in the image classification process and assist dermatologists to analyze the skin lesion area. Firstly, the feature detection method based on the extension theory can effectively quantify the difference between different colour features. Then, an interpretable segmentation edge of the skin lesion is established by using the neutrosophic theory which can convert the image into the neutrosophic space. The edge of the skin lesion is captured by applying the Hierarchical Gaussian Mixture Model (HGMM) method. Next, the edge sequence is established by segmenting the edge, and the contour regularity, symmetry, and uniformity of the edge of the skin lesion are analyzed. Finally, the extracted multi‐feature sets are used for dermoscopy image classification. Experiments are carried out on real datasets, and the classification accuracy of four kernel functions is verified. The experimental results show that the authors' method can effectively improve the classification accuracy of benign dermoscopy images and malignant dermoscopy images. [ABSTRACT FROM AUTHOR]

Skin cells grow in an orderly and controlled manner, in which old cells are pushed to the skin surface by healthy new cells, wherein they finally perish. However, when several cells damage DNA, new ones may start growing disorderly and can finally create cancer cells mass. Different types of skin cancer have been observed by experts. Although, melanoma, as one of the rarely happened cancers, is the most threatening type. Therefore, early detection of this type of cancer can be so useful for avoiding melanoma dangers and even helps to treat this type of cancer. The present paper proposes a new hierarchical procedure for the optimum diagnosis of melanoma cancer from dermoscopy images. Here, after image preprocessing, image segmentation is applied for the segmentation of the ROI. Then, the selection of features from the segmented images is performed and injected to a radial basis function‐based classifier to provide the final diagnosis of cancer. To deliver efficient results, the feature selection and the classifier have been optimized by a new design of Horse herd optimization algorithm (HHOA). The method is then implemented on the Society for Imaging Informatics in Medicine‐The International Skin Imaging Collaboration (SIIM‐ISIC) Melanoma dataset to validate its effectiveness and its achievements and also put in comparison with several different latest techniques. The results show that the proposed method with 95.46% precision provides the maximum performance among the others. [ABSTRACT FROM AUTHOR]

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients' lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%. [ABSTRACT FROM AUTHOR]

BACKGROUND: Dermoscopy is a diagnostic technique classically used for the study of pigmented skin lesions, nowadays, its use is gaining strength in inflammatory and infectious diseases, where it can reduce biopsies, and occasionally can give prognostic information. Pityriasis lichenoides chronica is an inflammatory skin disease, part of the papulosquamous dermatosis group. Its diagnosis requires histopathological confirmation. CLINICAL CASES: This paper reports the cases of four Latin-American patients with pityriasis lichenoides chronica and their dermoscopic characteristics. The first case corresponded to a phototype III 25-year-old woman, the second one to a phototype II 61-year-old woman, the third one to a phototype VI 34-year-old man and the fourth one to a phototype IV 36-year-old woman. CONCLUSIONS: In this study, the most frequent dermoscopic features were irregular punctate and linear vessels with clustered distribution, yellow-orange background, focal scales that can vary in color, and the absence of follicular detection. In addition, it was noted that dermoscopic findings may vary according to skin phototypes. [ABSTRACT FROM AUTHOR]

Because of the flexibility and availability of high-resolution digital cameras, dermatological photography is considered as a good alternative to dermoscopy. However, uneven background illumination on the dermatological photographs makes their automated analysis troublesome. Equalization of the uneven background illumination is helpful to make the automated analysis of the dermatological photographs more sensitive and specific. A customized algorithm for equalizing the uneven background illumination on dermatological photographs is proposed in this paper. The illumination-corrected image is reconstructed from the gamma-corrected illumination component in Hue Value Saturation (HSV) color space. The Retinex decomposition of the value component is formulated as a non-convex optimization problem. Constraints within the cost function are derived from the shading and texture priors. The shading and texture priors are computed respectively from the derivatives of the illumination and texture priors. On 137 dermatological photographs, the values of Average Gradient of the Illumination Component, Lightness Order Error, Sparse Feature Fidelity, Visual Saliency-based Index, Visual Information Fidelity and the computational time exhibited by the proposed devignetting algorithm are 0.1895 ± 0.0386, 232.9553 ± 140.7912, 0.9783 ± 0.0106, 0.9903 ± 0.0021, 0.7063 ± 0.0396 and 2.0272 ± 0.4319 (sec). The proposed algorithm is able to equalize the uneven background illumination without scaling or boosting it intolerably. It produces output images that are natural in appearance and free from structural/color artefacts. The loss of salient information is negligible in the proposed algorithm. It is computationally fast, as well. [ABSTRACT FROM AUTHOR]

Over the last three decades, the use of dermoscopy has been extended to inflammatory and infectious dermatoses. Regarding the latter, while the first applications concerned skin parasitoses, there has been a significant increase in the publication trend regarding nonparasitic dermatoses over recent years, yet data on this topic are sparse and often lack a standardized analytical approach. This systematic literature review summarizes published data on dermoscopy of bacterial, viral, and fungal dermatoses (dermoscopic findings, used setting, pathological correlation, and level of evidence of studies) and provides a homogeneous terminology of reported dermoscopic features according to a standardized methodology. A total of 152 papers addressing 43 different dermatoses and describing 184 different dermoscopic findings were included in the analysis. The majority of them displayed a level of evidence of V (107 single case reports and 40 case series), with only 5 studies showing a level of evidence of IV (case–control studies). Moreover, our analysis also underlined a high variability in the terminology used in published articles (even for the same dermatosis). Therefore, despite significant potential, future studies designed according to a systematic and standardized approach are required for a better characterization of dermoscopy of nonparasitic skin infections. [ABSTRACT FROM AUTHOR]

Clinically oriented deep learning algorithms, combined with large-scale medical datasets, have significantly promoted computer-aided diagnosis. To address increasing ethical and privacy issues, Federated Learning (FL) adopts a distributed paradigm to collaboratively train models, rather than collecting samples from multiple institutions for centralized training. Despite intensive research on FL, two major challenges are still existing when applying FL in the real-world medical scenarios, including the performance degradation (i.e., retrogress) after each communication and the intractable class imbalance. Thus, in this paper, we propose a novel personalized FL framework to tackle these two problems. For the retrogress problem, we first devise a Progressive Fourier Aggregation (PFA) at the server side to gradually integrate parameters of client models in the frequency domain. Then, at the client side, we design a Deputy-Enhanced Transfer (DET) to smoothly transfer global knowledge to the personalized local model. For the class imbalance problem, we propose the Conjoint Prototype-Aligned (CPA) loss to facilitate the balanced optimization of the FL framework. Considering the inaccessibility of private local data to other participants in FL, the CPA loss calculates the global conjoint objective based on global imbalance, and then adjusts the client-side local training through the prototype-aligned refinement to eliminate the imbalance gap with such a balanced goal. Extensive experiments are performed on real-world dermoscopic and prostate MRI FL datasets. The experimental results demonstrate the advantages of our FL framework in real-world medical scenarios, by outperforming state-of-the-art FL methods with a large margin. The source code is available at https://github.com/CityU-AIM-Group/PRR-Imbalance https://github.com/CityU-AIM-Group/PRR-Imbalance. [ABSTRACT FROM AUTHOR]

Simple Summary: According to skin disease reports by healthcare organizations, the number of cases of skin disease is growing gradually over the years globally. In skin disease diagnosis, dermatologists examine skin cells by using a dermatoscope. Due to the global shortage of expert dermatologists, mainly in developing countries, an accurate early skin disease diagnosis is not possible. To automate the examination of skin disease images, computer-aided diagnosis-based tools are used in healthcare and medical environments. Computer-aided diagnosis employs machine learning including deep learning models on skin disease images to detect and classify skin diseases. The present work proposes a deep learning-based model to accurately detect skin diseases and classify them into a family of skin diseases using skin disease images. The proposed system demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification compared to the existing deep learning-based models. The proposed computer-aided tool can be used as an early skin diagnosis tool to assist dermatologists in healthcare and medical environments. Deep learning-based models have been employed for the detection and classification of skin diseases through medical imaging. However, deep learning-based models are not effective for rare skin disease detection and classification. This is mainly due to the reason that rare skin disease has very a smaller number of data samples. Thus, the dataset will be highly imbalanced, and due to the bias in learning, most of the models give better performances. The deep learning models are not effective in detecting the affected tiny portions of skin disease in the overall regions of the image. This paper presents an attention-cost-sensitive deep learning-based feature fusion ensemble meta-classifier approach for skin cancer detection and classification. Cost weights are included in the deep learning models to handle the data imbalance during training. To effectively learn the optimal features from the affected tiny portions of skin image samples, attention is integrated into the deep learning models. The features from the finetuned models are extracted and the dimensionality of the features was further reduced by using a kernel-based principal component (KPCA) analysis. The reduced features of the deep learning-based finetuned models are fused and passed into ensemble meta-classifiers for skin disease detection and classification. The ensemble meta-classifier is a two-stage model. The first stage performs the prediction of skin disease and the second stage performs the classification by considering the prediction of the first stage as features. Detailed analysis of the proposed approach is demonstrated for both skin disease detection and skin disease classification. The proposed approach demonstrated an accuracy of 99% on skin disease detection and 99% on skin disease classification. In all the experimental settings, the proposed approach outperformed the existing methods and demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification. The proposed approach can be used as a computer-aided diagnosis (CAD) tool for the early diagnosis of skin cancer detection and classification in healthcare and medical environments. The tool can accurately detect skin diseases and classify the skin disease into their skin disease family. [ABSTRACT FROM AUTHOR]

Developing an automatic system for detection, segmentation, and classification of skin lesions is very useful to aid well-timed diagnosis of skin diseases. Lesion segmentation is a crucial task for automated diagnosis of skin cancers, as it affects significantly the accuracy of the subsequent steps. Varieties in sizes and locations of lesions, and the lesions with low-contrast boundaries make this task very challenging. In this paper, a three-stage CNN-based method is presented for accurate segmentation of lesions from dermoscopic images. At the first step, normalization, approximate locations and sizes of lesions are estimated. Due to the importance of the normalization stage, three CNN-based networks (Mask R-CNN, RetinaNet, and YOLOv3) are used for the lesion detection. A convolutional network is presented and used to combine the results of the object detection networks with a novel approach. The output of the first stage is a normalized cropped image containing the detected lesion in the center. At the second stage, segmentation, a CNN in a DeepLab3+ structure, is used to extract the lesion from the normalized image. Finally, an active contour method is used as the postprocessing to enhance the boundary of the segmented lesion. The proposed method is evaluated on well-known datasets. Experiments show that the proposed method outperforms all the previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Discoid lupus erythematosus (DLE) may lead to disfiguring scarring and permanent hair loss. Dermoscopy may serve as a noninvasive tool useful in the preliminary diagnosis of hair loss and inflammatory skin diseases. The aim of the paper was to summarize and analyze the dermoscopic features of DLE lesions in various anatomical locations. Methods: A systematic review of PubMed, Scopus and Web of Science was performed using the search terms: 'lupus' OR 'discoid lupus' OR 'cutaneous lupus' combined with 'dermoscopy' OR 'dermatoscopy' OR 'videodermoscopy' OR 'videodermatoscopy' OR 'trichoscopy' OR 'mucoscopy' OR 'onychoscopy'. Results: About 29 out of 318 initially identified papers were included in the analysis. In scalp DLE (n = 166), the most common findings were: white structureless areas (62%), arborizing vessels (57.8%), white scales (54.2%), follicular keratotic plugs (47%), absent follicular openings (45.8%), perifollicular scaling (43.9%), pink‐white background (40.4%), speckled brown pigmentation (38%), and fibrotic white dots (33.7%). In non‐scalp DLE (n = 129), the most frequent features were: follicular keratotic plugs (66.7%), white perifollicular halo (65.9%), white scale (39.5%), speckled brown pigmentation (38.8%), white structureless areas (37.2%), and arborizing vessels (34.9%). There are scarce data in the literature on dermoscopic findings in labial (n = 8), mucosal (n = 3) and ungual DLE (n = 1). Conclusions: DLE is characterized by a wide variety of dermoscopic findings with variable frequencies depending on the location of the lesions. Nevertheless, further studies are needed in order to reliably assess frequencies, correlation with disease stage and significance of individual dermoscopic features. [ABSTRACT FROM AUTHOR]

Background and Objective. Skin lesion classification by using deep learning technologies is still a considerable challenge due to high similarity among classes and large intraclass differences, serious class imbalance in data, and poor classification accuracy with low robustness. Approach. To address these issues, a two-stage framework for dermoscopy lesion classification using adversarial training and a fuzzy rank-based ensemble of multilayer feature fusion convolutional neural network (CNN) models is proposed. In the first stage, dermoscopy dataset augmentation based on generative adversarial networks is proposed to obtain realistic dermoscopy lesion images, enabling significant improvement for balancing the number of lesions in each class. In the second stage, a fuzzy rank-based ensemble of multilayer feature fusion CNN models is proposed to classify skin lesions. In addition, an efficient channel integrating spatial attention module, in which a novel dilated pyramid pooling structure is designed to extract multiscale features from an enlarged receptive field and filter meaningful information of the initial features. Combining the cross-entropy loss function with the focal loss function, a novel united loss function is designed to reduce the intraclass sample distance and to focus on difficult and error-prone samples to improve the recognition accuracy of the proposed model. Main results. In this paper, the common dataset (HAM10000) is selected to conduct simulation experiments to evaluate and verify the effectiveness of the proposed method. The subjective and objective experimental results demonstrate that the proposed method is superior over the state-of-the-art methods for skin lesion classification due to its higher accuracy, specificity and robustness. Significance. The proposed method effectively improves the classification performance of the model for skin diseases, which will help doctors make accurate and efficient diagnoses, reduce the incidence rate and improve the survival rates of patients. [ABSTRACT FROM AUTHOR]

Automatic segmentation of skin lesions is an important step in computer-aided diagnosis systems for melanoma detection. Although numerous methods have been proposed in the literature, this task is still a challenging issue due to the similarity between different lesions and complex visual characteristics that may be presented in the images. In this paper, we propose major modifications to the state-of-the-art U-Net structure to further improve its capability in skin lesion segmentation. These modifications are presented in both the encoding and the decoding paths. Instead of using only standard convolutional layers like U-Net, the proposed encoding path consists of 10 standard convolutional layers, which are inspired from the Visual Geometry Group (VGG16) network, followed by a pyramid pooling module and a dilated convolutional block. This combination enables to learn better representative feature maps and preserve more spatial resolution. Furthermore, dilated residual blocks are introduced in the decoding path to further refine the segmentation maps. The experimental results on three datasets including the IEEE International Symposium on Biomedical Imaging (ISBI) 2017, ISBI 2016, and PH2 showed that our proposed method has better performance than the basic U-Net, FCN, SegNet, and U-Net + + , and achieved the performance of state-of-the-art segmentation techniques, with minimum pre- and post-processing operations. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large intra-class differences, small inter-class differences, low contrast, and small and unbalanced datasets in dermoscopic images, this paper proposes a dermoscopic image classification method based on an ensemble of fine-tuned convolutional neural networks. By reconstructing the fully connected layers of the three pretrained models of Xception, ResNet50, and Vgg-16 and then performing transfer learning and fine-tuning the three pretrained models with the ISIC 2016 Challenge official skin dataset, we integrated the outputs of the three base models using a weighted fusion ensemble strategy in order to obtain a final prediction result able to distinguish whether a dermoscopic image indicates malignancy. The experimental results show that the accuracy of the ensemble model is 86.91%, the precision is 85.67%, the recall is 84.03%, and the F1-score is 84.84%, with these four evaluation metrics being better than those of the three basic models and better than some classical methods, proving the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings. [ABSTRACT FROM AUTHOR]

Background: In recent years, melanoma is rising at a faster rate compared to other cancers. Although it is the most serious type of skin cancer, the diagnosis at early stages makes it curable. Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin. In the last few decades, digital imaging devices have made great progress which allowed capturing and storing high‐quality images from these examinations. The stored images are now being standardized and used for the automatic detection of melanoma. However, when the hair covers the skin, this makes the task challenging. Therefore, it is important to eliminate the hair to get accurate results. Methods: In this paper, we propose a simple yet efficient method for hair removal using a variational autoencoder without the need for paired samples. The encoder takes as input a dermoscopy image and builds a latent distribution that ignores hair as it is considered noise, while the decoder reconstructs a hair‐free image. Both encoder and decoder use a decent convolutional neural networks architecture that provides high performance. The construction of our model comprises two stages of training. In the first stage, the model has trained on hair‐occluded images to output hair‐free images, and in the second stage, it is optimized using hair‐free images to preserve the image textures. Although the variational autoencoder produces hair‐free images, it does not maintain the quality of the generated images. Thus, we explored the use of three‐loss functions including the structural similarity index (SSIM), L1‐norm, and L2‐norm to improve the visual quality of the generated images. Results: The evaluation of the hair‐free reconstructed images is carried out using t‐distributed stochastic neighbor embedding (SNE) feature mapping by visualizing the distribution of the real hair‐free images and the synthesized hair‐free images. The conducted experiments on the publicly available dataset HAM10000 show that our method is very efficient. [ABSTRACT FROM AUTHOR]

The skin, one of the most crucial organs of the human body, serves as a barrier between the body and the external environment. Early detection of skin diseases is imperative to reduce mortality rates, as some untreated conditions may progress to skin cancer. Segmentation and classification of lesions represent pivotal and interrelated endeavors within the realm of skin disease diagnosis. For this purpose, this paper presents a comprehensive diagnostic framework for segmentation and classification of skin lesions, which integrates a Legendre multiwavelet transform-based fusion XNet (LW-XNet) with an improved soft attention dense connection convolutional network (ISA-DenseNet). LW-XNet combines the strengths of XNet in fusing different frequency components of images, along with the strong feature representation capability of LW bases with various regularities for overall contextual information and detailed information of dermoscopy images. Furthermore, its encoder devises a LWT channel concatenate (LCC) block to subdivide the image into eight wavelet coefficient feature images and perform concatenated processing on them, enabling it to better differentiate and comprehend the intricate features within dermoscopy images. Finally, ISA-DenseNet is utilized for multi-class classification of the segmented images. Experimental results demonstrate the superiority of the proposed framework over existing segmentation and classification methods. [ABSTRACT FROM AUTHOR]

Purpose: Melanoma is known as the most aggressive form of skin cancer and one of the fastest growing malignant tumors worldwide. Several computer‐aided diagnosis systems for melanoma have been proposed, still, the algorithms encounter difficulties in the early stage of lesions. This paper aims to discriminate melanoma and benign skin lesion in dermoscopic images. Methods: The proposed algorithm is based on the color and texture of skin lesions by introducing a novel feature extraction technique. The algorithm uses an automatic segmentation based on k‐means generating a fairly accurate mask for each lesion. The feature extraction consists of the existing and novel color and texture attributes measuring how color and texture vary inside the lesion. To find the optimal results, all the attributes are extracted from lesions in five different color spaces (RGB, HSV, Lab, XYZ, and YCbCr) and used as the inputs for three classifiers (K nearest neighbors, support vector machine , and artificial neural network). Results: The PH2 set is used to assess the performance of the proposed algorithm. The results of our algorithm are compared to the results of published articles that used the same dataset, and it shows that the proposed method outperforms the state of the art by attaining a sensitivity of 99.25%, specificity of 99.58%, and accuracy of 99.51%. Conclusion: The final results show that the colors combined with texture are powerful and relevant attributes for melanoma detection and show improvement over the state of the art. [ABSTRACT FROM AUTHOR]

Background/Objectives: Non‐invasive skin imaging features of main skin inflammatory and autoimmune diseases have been reported, although a comprehensive review of their correlation with histopathologic features is currently lacking. Therefore, the aim of this paper was to review the correlation of dermoscopic, reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) criteria of main inflammatory and autoimmune skin diseases with their corresponding histopathologic criteria correlation. Methods: Studies on human subjects affected by main inflammatory and autoimmune diseases, defining the correlation of dermoscopic, RCM or OCT with histopathologic criteria, were included in the review. Five groups of diseases were identified and described: psoriasiform, spongiotic and interface dermatitis, bullous diseases and scleroderma. Results: Psoriasiform dermatitis was typified by white scales, corresponding to hyperkeratosis, and vessels, observed with RCM and OCT. Spongiosis, corresponding to dark areas within the epidermis with RCM and OCT, was the main feature of spongiotic dermatitis. Interface dermatitis was characterised by dermoepidermal junction obscuration. Blisters, typical of bullous diseases, were visualised as dark areas with RCM and OCT while scleroderma lesions were characterised by dermoscopic fibrotic beams, related to dermal thickness variations, with specific OCT and histopathologic correlations. Conclusions: Although the role of RCM and OCT has yet to be defined in clinical practice, non‐invasive skin imaging shows promising results on inflammatory and autoimmune skin diseases, due to the correlation with histopathologic features. [ABSTRACT FROM AUTHOR]

Research interest in dermoscopy is increasing, but the complete dermoscopic image sets used in inter-observer studies of skin tumours are not often shared in research publications. The aim of this systematic review was to analyse what proportion of images depicting skin tumours are published in studies investigating inter-observer variations in the assessment of dermoscopic features and/or patterns. Embase, MEDLINE and Scopus databases were screened for eligible studies published from inception to 2 July 2020. For included studies the proportion of lesion images presented in the papers and/or supplements was extracted. A total of 61 studies (53 original studies and 8 shorter reports (i.e. research letters or concise reports)). published in the period 1997 to 2020 were included. These studies combined included 14,124 skin tumours, of which 373 (3%) images were published. This systematic review highlights that the vast majority of images included in dermoscopy research are not published. Data sharing should be a requirement for future studies, and must be enabled and standardized by the dermatology research community and editorial offices. [ABSTRACT FROM AUTHOR]

The non‐invasive examination of the nail unit using a dermoscope is known as onychoscopy. This technique has become increasingly appreciated to facilitate the clinical diagnosis of nail disorders, opening up a valuable second front with a potential to avoid invasive diagnostic procedures. During a nail consultation, the nail unit should always be examined with the aid of a dermatoscope in all its components. The aim of this paper was to provide practical information about onychoscopy of the nail plate free edge and hyponychium, two components of the nail unit difficult to evaluate at naked eye and often forgotten, but of paramount importance. [ABSTRACT FROM AUTHOR]

Dermoscopy and reflectance confocal microscopy (RCM) are two noninvasive, optical imaging tools used to facilitate clinical diagnosis. A biopsy technique that produces exact correlation with optical imaging features is not previously reported. To evaluate the applications of a novel feature-focused 'precision biopsy' technique that correlates clinical–dermoscopy–RCM findings with histopathology. This was a prospective case-series performed during August 2017 and June 2019 at a tertiary care cancer. We included consecutive patients requiring a precise dermoscopy–RCM–histopathologic correlation. We performed prebiopsy dermoscopy and both wide probe and handheld RCM of suspicious lesions. Features of interest were isolated with the aid of paper rings and a 2 mm punch biopsy was performed in the dermoscopy- or RCM-highlighted area. Tissue was processed either en face or with vertical sections. One-to-one correlation with histopathology was obtained. Twenty-three patients with 24 lesions were included in the study. The mean age was 64.6 years (range 22–91 years); there were 16 (69.6%) males, 14 (58.3%) lesions biopsied were on head and neck region. We achieved tissue-conservation diagnosis in 100% (24/24), 13 (54.2%) were clinically equivocal lesions, six (25%) were selected for 'feature correlation' of structures on dermoscopy or RCM, and five (20.8%) for 'correlation of new/unknown' RCM features seen on follow-up. The precision biopsy technique described herein is a novel method that facilitates direct histopathological correlation of dermoscopy and RCM features. With the aids of optical imaging devices, accurate diagnosis may be achieved by minimally invasive tissue extraction. [ABSTRACT FROM AUTHOR]

Background: Dermatofibroma, also known as cutaneous benign fibrous histiocytoma, is a common skin tumour. Aim: The aim of this paper was to present a rare variant of dermatofibroma, atrophic dermatofibroma, emphasizing histopathological and dermoscopic features. Patients/Methods: A case of atrophic dermatofibroma in a female patient with the characteristic histopathological features and newly demonstrated dermoscopic findings is presented. Results: A 54‐year‐old female presented with a depressed reddish lesion on the back showing histopathological findings of atrophic dermatofibroma. The dermoscopy of the lesion revealed a peripheral pigment network surrounding a pink‐reddish colouration around a central whitish scar‐like patch with white‐yellow scales which was not an exact match with the description in the literature. Conclusion: Atrophic dermatofibroma is a rare variant that presents as an atrophic, depressed skin lesion which can easily be overlooked. Atrophic dermatofibroma should be considered in the differential diagnoses of atrophic, depressed lesions on the upper body of middle‐aged women. The case of atrophic dermatofibroma presented here showed typical histopathologic findings with atypical dermoscopic features. [ABSTRACT FROM AUTHOR]

Aim: Pediatric age period has a dynamic structure for nevogenesis. In this paper, it was aimed to contribute to recognition of banal and atypical nevi through examining dermoscopic pattern and pigment network structures of acquired melanocytic nevi in pediatric ag e group. Patients and Methods: Our study was a prospective conducted between june 2011 and june 2013. One hundred and fifty pediatric volunteers who were not predisposed to nevogenesis were included in the study. Children were divided into two groups as 7 and under, and 8 and over. Results: It was observed that the mean number of nevus was higher in the older age group (9.72) than in the younger age group (3.44). It was observed that the predominant pattern structure was globular pattern in both groups, and nevi with reticular patter in the age group of 8 and above were more than the age group 7 and below (p = 0.03). In both groups, nevi with globular pattern were found to be denser in the trunk and nevi with reticular patter in the extremities (p = 0.001). When the pigment network structures of nevi were examined, it was observed that the most frequently observed pigment network was uniform, but nevi with central pigmentation changes were observed more frequently in the age group of 8 years and older (p = 0.001). Conclusion: Although acquired melanocytic nevi in the pediatric age group often have a globular pattern and uniform pigment network, the number of nevi with reticular pattern and central pigmentation changes increases with increasing age. [ABSTRACT FROM AUTHOR]

Background: A plethora of diseases manifest as acquired genital lymphangiectasias which clinically manifest as superficial vesicles. They range from infections such as tuberculosis to connective tissue diseases such as scleroderma and even malignancy. Amongst infectious etiologies, lymphatic filariasis leads as the cause for lymphatic obstruction. Despite this, acquired lymphangiectasias due to this cause are not commonly reported. An unusual case of acquired scrotal lymphangiectasia secondary to filariasis is detailed in this paper with dermoscopic and histologic findings. Methods: A 65-year-old male farmer presented with multiple, asymptomatic vesicles over the scrotum with thickened scrotal and penile skin that had occurred for six years. He gave past history of intermittent fever and milky urine, was diagnosed with filariasis and treated with diethylcarbamazine for a year, four years previously. Systemic complaints abated but the peno-scrotal lesions did not. Results: Polarized dermoscopy revealed multiple skin-colored nodules and translucent pale blue lacunae over the scrotum. A few radially arranged linear irregular vessels were noted over the nodules. On histopathology, multiple ectatic lymphatics were noted in the mid and upper dermis with acanthosis and superficial perivascular lymphocytes. Peripheral smear revealed eosinophils; however, microfilariae could not be detected despite repeated diethylcarbamazine provocation and night smears being taken. The findings were compatible with acquired scrotal lymphangiectasia secondary to treated lymphatic filariasis. Local hygiene was advised; however, procedural treatments were refused by the patient. Conclusion: Herein, we report an unusual case of acquired scrotal lymphangiectasia of the scrotum secondary to treated lymphatic filariasis. Very few similar reports exist. To the best of our knowledge, dermoscopic features of this condition have not been elucidated before. This case, detailing an uncommon manifestation of a common disease (filariasis), demonstrates the importance of careful history taking and examination. This was especially so in the present case since only circumstantial evidence of filariasis was noted in investigations. There is a need to heighten awareness of this unusual condition amongst physicians especially if the patient hails from an area endemic for filariasis. [ABSTRACT FROM AUTHOR]

Melanoma is widely known as one of the most dangerous cancers. Over the past few decades, technological improvements have made it possible to introduce more advanced diagnostic tools for melanoma. Unfortunately, even though better tools are available, diagnosis accuracy is still unsatisfactory. Hundreds of papers have been published containing ideas on how to improve melanoma diagnosis accuracy, including a range of imaging and image analysis techniques. Some of the best diagnosis results are obtained using multi-level SIAscope images, but even with this method there is still room for further improvement. In this paper, we propose the use of additional discriminative features such as box dimension and lacunarity calculated based on a multilevel image database. The goal of this paper is to show the usefulness of fractal methods used with multilevel images and binarization methods in skin cancer pattern recognition. The results were compared to an assessment of each feature of Hunter’s scoring method, which is commonly used as a diagnostic indicator by doctors. The results indicate the usefulness of the fractal characteristics of the geometric shapes of lesions or specific parts of them. Compared to other research, the presented results clearly indicate that fractal lesion characteristics can be used as one of the features taken into account in the diagnostic process. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion. [ABSTRACT FROM AUTHOR]

Besides the well-known use in supporting the non-invasive diagnosis of non-tumoral dermatoses (general dermatology), dermoscopy has been shown to be a promising tool also in predicting and monitoring therapeutic outcomes of such conditions, with the consequent improvement/optimization of their treatment. In the present paper, we sought to provide an up-to-date overview on the use of dermoscopy in highlighting response predictor factors and evaluating therapeutic results in the field of general dermatology according to the current literature data. Several dermatoses may somehow benefit from such applications, including inflammatory conditions (psoriasis, lichen planus, dermatitis, granulomatous conditions, erythro-telangiectatic rosacea, Zoon balanitis and vulvitis, cutaneous mastocytosis, morphea and extra-genital lichen sclerosus), pigmentary disorders (vitiligo and melasma) and infectious dermatoses (scabies, pediculosis, demodicosis and viral warts). [ABSTRACT FROM AUTHOR]

Efficient skin lesion segmentation algorithms are required for computer aided diagnosis of skin cancer. Several algorithms were proposed for skin lesion segmentation. The existing algorithms are short of achieving ideal performance. In this paper, a novel semi-automatic segmentation algorithm is proposed. The fare concept of the proposed is 8-directional search based on threshold for lesion pixel, starting from a user provided seed point. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. In comparison to a chosen set of algorithms, the proposed approach gives high accuracy and specificity values. A significant advantage of the proposed method is the ability to deal with discontinuities in the lesion. [ABSTRACT FROM AUTHOR]

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced–SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way. [ABSTRACT FROM AUTHOR]

Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases. [ABSTRACT FROM AUTHOR]

Despite the potential of teledermatology to increase access to dermatology services and improve patient care, it is not widely practised in Australia. In an effort to increase uptake of teledermatology, Australian‐specific practice guidelines for teledermatology are being developed by the Australasian College of Dermatologist. This paper reports finding from literature reviews that were undertaken to inform the development of these guidelines. Results cover the following sections: Modalities of teledermatology; Patient selection and consent; Imaging; Quality and safety; Privacy and security; Communication; and Documentation and retention. The document educates providers about the benefits and limitations of telehealth while articulating how to enhance patient care and reduce risk when practicing teledermatology. [ABSTRACT FROM AUTHOR]