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Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial–textural–frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model's performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions. This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features. Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases. This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. The SASAN dataset serves as a valuable tool for researchers aiming to improve such systems and ultimately contribute to better diagnostic outcomes. [ABSTRACT FROM AUTHOR]

Skin cancer is a prevalent and perilous form of cancer and presents significant diagnostic challenges due to its high costs, dependence on medical experts, and time-consuming procedures. The existing diagnostic process is inefficient and expensive, requiring extensive medical expertise and time. To tackle these issues, researchers have explored the application of artificial intelligence (AI) tools, particularly machine learning techniques such as shallow and deep learning, to enhance the diagnostic process for skin cancer. These tools employ computer algorithms and deep neural networks to identify and categorize skin cancer. However, accurately distinguishing between skin cancer and benign tumors remains challenging, necessitating the extraction of pertinent features from image data for classification. This study addresses these challenges by employing Principal Component Analysis (PCA), a dimensionality-reduction approach, to extract relevant features from skin images. Additionally, accurately classifying skin images into malignant and benign categories presents another obstacle. To improve accuracy, the AdaBoost algorithm is utilized, which amalgamates weak classification models into a robust classifier with high accuracy. This research introduces a novel approach to skin cancer diagnosis by integrating Principal Component Analysis (PCA), AdaBoost, and EfficientNet B0, leveraging artificial intelligence (AI) tools. The novelty lies in the combination of these techniques to develop a robust and accurate system for skin cancer classification. The advantage of this approach is its ability to significantly reduce costs, minimize reliance on medical experts, and expedite the diagnostic process. The developed model achieved an accuracy of 93.00% using the DermIS dataset and demonstrated excellent precision, recall, and F1-score values, confirming its ability to correctly classify skin lesions as malignant or benign. Additionally, the model achieved an accuracy of 91.00% using the ISIC dataset, which is widely recognized for its comprehensive collection of annotated dermoscopic images, providing a robust foundation for training and validation. These advancements have the potential to significantly enhance the efficiency and accuracy of skin cancer diagnosis and classification. Ultimately, the integration of AI tools and techniques in skin cancer diagnosis can lead to cost reduction and improved patient outcomes, benefiting both patients and healthcare providers. [ABSTRACT FROM AUTHOR]

Abstract Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection of three compact CNNs and Gabor Wavelets (GW) to acquire a comprehensive feature vector consisting of spatial–textural–frequency attributes. SCaLiNG gathers a wide range of image details by breaking down these photos into multiple directional sub-bands using GW, and then learning several CNNs using those sub-bands and the original picture. SCaLiNG also combines attributes taken from various CNNs trained with the actual images and subbands derived from GW. This fusion process correspondingly improves diagnostic accuracy due to the thorough representation of attributes. Furthermore, SCaLiNG applies a feature selection approach which further enhances the model’s performance by choosing the most distinguishing features. Experimental findings indicate that SCaLiNG maintains a classification accuracy of 0.9170 in categorising SC subcategories, surpassing conventional single-CNN models. The outstanding performance of SCaLiNG underlines its ability to aid dermatologists in swiftly and precisely recognising and classifying SC, thereby enhancing patient outcomes.

Abstract Worldwide, skin cancer prevalence necessitates accurate diagnosis to alleviate public health burdens. Although the application of artificial intelligence in image analysis and pattern recognition has improved the accuracy and efficiency of early skin cancer diagnosis, existing supervised learning methods are limited due to their reliance on a large amount of labeled data. To overcome the limitations of data labeling and enhance the performance of diagnostic models, this study proposes a semi-supervised skin cancer diagnostic model based on Self-feedback Threshold Focal Learning (STFL), capable of utilizing partial labeled and a large scale of unlabeled medical images for training models in unseen scenarios. The proposed model dynamically adjusts the selection threshold of unlabeled samples during training, effectively filtering reliable unlabeled samples and using focal learning to mitigate the impact of class imbalance in further training. The study is experimentally validated on the HAM10000 dataset, which includes images of various types of skin lesions, with experiments conducted across different scales of labeled samples. With just 500 annotated samples, the model demonstrates robust performance (0.77 accuracy, 0.6408 Kappa, 0.77 recall, 0.7426 precision, and 0.7462 F1-score), showcasing its efficiency with limited labeled data. Further, comprehensive testing validates the semi-supervised model’s significant advancements in diagnostic accuracy and efficiency, underscoring the value of integrating unlabeled data. This model offers a new perspective on medical image processing and contributes robust scientific support for the early diagnosis and treatment of skin cancer.

Worldwide, skin cancer prevalence necessitates accurate diagnosis to alleviate public health burdens. Although the application of artificial intelligence in image analysis and pattern recognition has improved the accuracy and efficiency of early skin cancer diagnosis, existing supervised learning methods are limited due to their reliance on a large amount of labeled data. To overcome the limitations of data labeling and enhance the performance of diagnostic models, this study proposes a semi-supervised skin cancer diagnostic model based on Self-feedback Threshold Focal Learning (STFL), capable of utilizing partial labeled and a large scale of unlabeled medical images for training models in unseen scenarios. The proposed model dynamically adjusts the selection threshold of unlabeled samples during training, effectively filtering reliable unlabeled samples and using focal learning to mitigate the impact of class imbalance in further training. The study is experimentally validated on the HAM10000 dataset, which includes images of various types of skin lesions, with experiments conducted across different scales of labeled samples. With just 500 annotated samples, the model demonstrates robust performance (0.77 accuracy, 0.6408 Kappa, 0.77 recall, 0.7426 precision, and 0.7462 F1-score), showcasing its efficiency with limited labeled data. Further, comprehensive testing validates the semi-supervised model's significant advancements in diagnostic accuracy and efficiency, underscoring the value of integrating unlabeled data. This model offers a new perspective on medical image processing and contributes robust scientific support for the early diagnosis and treatment of skin cancer. [ABSTRACT FROM AUTHOR]

This article discusses a novel approach to non-invasive skin cancer diagnosis using graphene electrical tattoos and electrical impedance tomography (EIT). The authors propose a system that combines EIT with graphene electronic tattoos (GETs) to differentiate between benign and malignant skin lesions based on impedance differences. The feasibility of the system is demonstrated through computational and experimental models. This approach has the potential to provide rapid and accurate skin cancer diagnosis without invasive procedures. The article also provides a list of references related to skin cancer detection and diagnosis using electrical impedance techniques, which can be valuable resources for further research. [Extracted from the article]

Background The global impact of skin cancer has underscored the urgency of accurate and timely detection for effective treatment. In recent years, the medical research landscape has witnessed a rapid evolution fueled by the integration of Machine Learning (ML) and Deep Learning (DL) models, specifically aimed at enhancing skin cancer diagnosis and classification. Methods This comprehensive exploration delves into the forefront of advancements, focusing on the strategic application of ML and DL algorithms across critical facets of skin cancer management, encompassing detection, classification, and prognosis. By synthesizing diverse studies and emerging developments, this review aims to provide an all-encompassing perspective of the current landscape in skin cancer research, underpinned by the capabilities of ML and DL models. Results The synthesis of research outcomes within this review accentuates the remarkable progress achieved through the fusion of ML and DL methodologies. These achievements manifest as heightened accuracy and efficiency in skin cancer diagnosis and classification, offering invaluable support to healthcare professionals. The integration of these algorithmic approaches has ushered in improved patient outcomes, facilitating prompt interventions and tailored treatment strategies. Conclusions At its core, this review strives to equip researchers, clinicians, and healthcare providers with an intricate comprehension of the existing terrain in skin cancer research, driven by the prowess of ML and DL models. By spotlighting accomplishments alongside untapped prospects for refinement, this endeavor seeks to inspire fresh breakthroughs in the domain of skin cancer detection. [ABSTRACT FROM AUTHOR]

Skin cancer is a common and potentially fatal disease that necessitates early and precise diagnosis for effective treatment. In recent years, artificial intelligence has shown promise in aiding dermatologists in the diagnosis of skin cancer. However, the inability of AI models to be interpreted hinders their adoption in clinical practice. This paper presents the design of an AI-based architecture for the real-time diagnosis of skin cancer in an effort to address such issues. The proposed system employs a collection of artificial intelligence (AI) models, including Decision Trees, Rule-Based Models, Logistic Regression, and Deep Forest Models, to achieve accurate and interpretable skin cancer diagnosis. Each model contributes its strengths to the ensemble, thereby enhancing the performance and interpretability of the whole. The ensemble method combines the benefits of various models to compensate for their shortcomings. The effectiveness of the proposed system is demonstrated by the analysis of a Skin Cancer MNIST, ISIC, and Mendeley Skin Cancer Datasets with nearly 250K samples, with 98.9% accuracy, 99.5% precision, and 98.5% recall. The system outperforms existing skin cancer diagnosis methods. The achieved accuracy and performance metrics indicate the system's potential as a reliable real-time diagnostic tool for dermatologists. The proposed system's use cases are diverse. Dermatologists can use the real-time skin cancer diagnosis system to accelerate the screening process, improve diagnostic accuracy, and improve patient outcomes in clinical settings. The models are selected for their ability to capture complex relationships in data, with each model contributing its individual strengths to the ensemble, thereby enhancing the performance and interpretability of the whole. In addition, the system can be integrated into telemedicine platforms, allowing remote patients to receive preliminary assessments and guidance from AI models prior to seeking additional medical care scenarios. [ABSTRACT FROM AUTHOR]

Cancer is one of the diseases, caused by cell divisions that can be fatal. It has been the second most common cause of death for the past period of years globally. Any region of the body can be affected by a wide range of disorders collectively referred to as cancer. Neoplasms as well as malignant tumors are other terms that are used for describing cancer. Skin is one of the body parts that can be affected. Early melanoma skin cancer detection is a must so that the mortality rate of skin cancer patients is decreased. The accuracy of early detection of melanoma skin cancer can be enhanced through applying machine learning methods. This paper provides a model that can detect melanoma skin cancer early. This model is built using the dataset that the International Skin Imaging Collaboration has provided. The proposed model using support vector machines achieved a promising accuracy of 95.96%. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) with different architectures have shown promising results in skin cancer diagnosis. However, CNN has a high computational cost, which makes the need for a light version of CNN a desirable step. This version can be used on small devices, such as mobile phones or tablets. A light version can be created using pruning techniques. In this study, iterative magnitude pruning (IMP) is utilized. This method depends on pruning the network iteratively. The IMP method is applied on AlexNet with transfer learning (TL) and data augmentation. The proposed IMP AlexNet with TL is applied on three different skin cancer datasets which are PAD-UFES-20, MED-NODE, and PH2 dataset. The datasets used are a combination of smartphone, dermoscopic, and non-dermoscopic images. Different CNN versions are applied on the same datasets for comparison with IMP AlexNet. The CNNs used are VGG-16, ShuffleNet, SqueezNet, DarkNet-19, DarkNet-53, and Inception-v3. The proposed IMP AlexNet achieved accuracies of 97.62%, 96.79%, and 96.75%, with accuracy losses of 1.53%, 2.3%, and 2.2%, respectively, compared to the original AlexNet. In addition, the proposed IMP AlexNet requires less running time and memory usage than the traditional AlexNet. The average running time for IMP AlexNet is 0.45 min, 0.28 min, and 0.3 min, for PAD-UFES-20, MED-NODE, and PH2 datasets, respectively. The average RAM usage with IMP AlexNet is 1.8 GB, 1.6 GB, and 1.7 GB, respectively. IMP AlexNet accelerates the average running time by approximately 15 times that of the traditional AlexNet and reduces the average RAM used by 40%. [ABSTRACT FROM AUTHOR]

Skin cancer is a prevalent and perilous form of cancer and presents significant diagnostic challenges due to its high costs, dependence on medical experts, and time-consuming procedures. The existing diagnostic process is inefficient and expensive, requiring extensive medical expertise and time. To tackle these issues, researchers have explored the application of artificial intelligence (AI) tools, particularly machine learning techniques such as shallow and deep learning, to enhance the diagnostic process for skin cancer. These tools employ computer algorithms and deep neural networks to identify and categorize skin cancer. However, accurately distinguishing between skin cancer and benign tumors remains challenging, necessitating the extraction of pertinent features from image data for classification. This study addresses these challenges by employing Principal Component Analysis (PCA), a dimensionality-reduction approach, to extract relevant features from skin images. Additionally, accurately classifying skin images into malignant and benign categories presents another obstacle. To improve accuracy, the AdaBoost algorithm is utilized, which amalgamates weak classification models into a robust classifier with high accuracy. This research introduces a novel approach to skin cancer diagnosis by integrating Principal Component Analysis (PCA), AdaBoost, and EfficientNet B0, leveraging artificial intelligence (AI) tools. The novelty lies in the combination of these techniques to develop a robust and accurate system for skin cancer classification. The advantage of this approach is its ability to significantly reduce costs, minimize reliance on medical experts, and expedite the diagnostic process. The developed model achieved an accuracy of 93.00% using the DermIS dataset and demonstrated excellent precision, recall, and F1-score values, confirming its ability to correctly classify skin lesions as malignant or benign. Additionally, the model achieved an accuracy of 91.00% using the ISIC dataset, which is widely recognized for its comprehensive collection of annotated dermoscopic images, providing a robust foundation for training and validation. These advancements have the potential to significantly enhance the efficiency and accuracy of skin cancer diagnosis and classification. Ultimately, the integration of AI tools and techniques in skin cancer diagnosis can lead to cost reduction and improved patient outcomes, benefiting both patients and healthcare providers.

Simple Summary: The Internet of Things (IoT) uses connected devices and sensors, like high-resolution cameras and specific sensors in wearable devices, for the collection of skin images with abnormalities. Skin cancer detection is difficult because of differences in lesion size, shape, and lighting conditions. To address this, an innovative approach called "ODL-SCDC", combining deep learning with IoT technology, is developed. The proposed model uses advanced techniques like hyperparameter selection and feature extraction to improve skin cancer classification. The results show that ODL-SCDC outperforms other methods in accurately identifying skin lesions, which could have a significant impact on early cancer detection in the medical field. Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process. [ABSTRACT FROM AUTHOR]

Skin cancer, particularly melanoma, has been recognized as one of the most lethal forms of cancer. Detecting and diagnosing skin lesions accurately can be challenging due to the striking similarities between the various types of skin lesions, such as melanoma and nevi, especially when examining the color images of the skin. However, early diagnosis plays a crucial role in saving lives and reducing the burden on medical resources. Consequently, the development of a robust autonomous system for skin cancer classification becomes imperative. Convolutional neural networks (CNNs) have been widely employed over the past decade to automate cancer diagnosis. Nonetheless, the emergence of the Vision Transformer (ViT) has recently gained a considerable level of popularity in the field and has emerged as a competitive alternative to CNNs. In light of this, the present study proposed an alternative method based on the off-the-shelf ViT for identifying various skin cancer diseases. To evaluate its performance, the proposed method was compared with 11 CNN-based transfer learning methods that have been known to outperform other deep learning techniques that are currently in use. Furthermore, this study addresses the issue of class imbalance within the dataset, a common challenge in skin cancer classification. In addressing this concern, the proposed study leverages the vision transformer and the CNN-based transfer learning models to classify seven distinct types of skin cancers. Through our investigation, we have found that the employment of pre-trained vision transformers achieved an impressive accuracy of 92.14%, surpassing CNN-based transfer learning models across several evaluation metrics for skin cancer diagnosis. [ABSTRACT FROM AUTHOR]

Although various skin cancer detection devices have been proposed, most of them are not used owing to their insufficient diagnostic accuracies. Laser-induced plasma spectroscopy (LIPS) can noninvasively extract biochemical information of skin lesions using an ultrashort pulsed laser. To investigate the diagnostic accuracy and safety of real-time noninvasive in vivo skin cancer diagnostics utilizing nondiscrete molecular LIPS combined with a deep neural network (DNN)–based diagnostic algorithm. In vivo LIPS spectra were acquired from 296 skin cancers (186 basal cell carcinomas, 96 squamous cell carcinomas, and 14 melanomas) and 316 benign lesions in a multisite clinical study. The diagnostic performance was validated using 10-fold cross-validations. The sensitivity and specificity for differentiating skin cancers from benign lesions using LIPS and the DNN-based algorithm were 94.6% (95% CI: 92.0%-97.2%) and 88.9% (95% CI: 85.5%-92.4%), respectively. No adverse events, including macroscopic or microscopic visible marks or pigmentation due to laser irradiation, were observed. The diagnostic performance was evaluated using a limited data set. More extensive clinical studies are needed to validate these results. This LIPS system with a DNN-based diagnostic algorithm is a promising tool to distinguish skin cancers from benign lesions with high diagnostic accuracy in real clinical settings. [ABSTRACT FROM AUTHOR]

Diagnosing melanocytic lesions is one of the most challenging areas of pathology with extensive intra- and inter-observer variability. The gold standard for a diagnosis of invasive melanoma is the examination of histopathological whole slide skin biopsy images by an experienced dermatopathologist. Digitized whole slide images offer novel opportunities for computer programs to improve the diagnostic performance of pathologists. In order to automatically classify such images, representations that reflect the content and context of the input images are needed. In this paper, we introduce a novel self-attention-based network to learn representations from digital whole slide images of melanocytic skin lesions at multiple scales. Our model softly weighs representations from multiple scales, allowing it to discriminate between diagnosis-relevant and -irrelevant information automatically. Our experiments show that our method outperforms five other state-of-the-art whole slide image classification methods by a significant margin. Our method also achieves comparable performance to 187 practicing U.S. pathologists who interpreted the same cases in an independent study. To facilitate relevant research, full training and inference code is made publicly available at https://github.com/meredith-wenjunwu/ScATNet.

Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light–tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device. [ABSTRACT FROM AUTHOR]

If found and treated early, fast-growing skin cancers can dramatically prolong patients' lives. Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image segmentation is a part of image preprocessing and essential technical support in the process of image processing. In addition, multi-threshold image segmentation (MIS) technology is extensively used due to its straightforward and effective features. Many academics have coupled different meta-heuristic algorithms with MIS to raise image segmentation quality. Nonetheless, these meta-heuristic algorithms frequently enter local optima. Therefore, this paper suggests an improved salp swarm algorithm (ILSSA) method that combines iterative mapping and local escaping operator to address this drawback. Besides, this paper also proposes the ILSSA-based MIS approach, which is triumphantly utilized to segment dermoscopic images of skin cancer. This method uses two-dimensional (2D) Kapur's entropy as the objective function and employs non-local means 2D histogram to represent the image information. Furthermore, an array of benchmark function test experiments demonstrated that ILSSA could alleviate the local optimal problem more effectively than other compared algorithms. Afterward, the skin cancer dermoscopy image segmentation experiment displayed that the proposed ILSSA-based MIS method obtained superior segmentation results than other MIS peers and was more adaptable at different thresholds. [ABSTRACT FROM AUTHOR]

Malignant melanoma is a very aggressive tumour with the ability to metastasize at an early stage. Therefore, early detection is of great importance. Multiphoton tomography is a new non-invasive examination method in the clinical diagnosis of skin alterations that can be used for such early diagnosis. In this paper, a method for automated evaluation of multiphoton images of the skin is presented. The following features at the cellular and subcellular level were extracted to differentiate between malignant melanomas, lesions, and healthy skin: cell symmetry, cell distance, cell density, cell and nucleus contrast, nucleus cell ratio, and homogeneity of cytoplasm. The extracted features formed the basis for the subsequent classification. Two feature sets were used. The first feature set included all the above-mentioned features, while the second feature set included the significantly different features between the three classes resulting from a multivariate analysis of variance. The classification was performed by a Support Vector Machine, the k-Nearest Neighbour algorithm, and Ensemble Learning. The best classification results were obtained with the Support Vector Machine using the first feature set with an accuracy of 52 % and 79.6 % for malignant melanoma and healthy skin, respectively. Despite the small number of subjects investigated our results indicate that the proposed automatic method can differentiate malignant melanoma, lesions, and healthy skin. For future clinical application, an extended study with more multiphoton images is needed.

Background: Basal cell carcinoma (BCC) is the most common skin cancer, accounting for approximately 80% of nonmelanoma skin cancer diagnoses each year. Among other factors, the staging of BCC is influenced by its measured diameter. Stage 1 BCC is defined as a lesion measuring 2 cm across or less. Of note, there have been increasing publications reporting features of "small‐sized" BCCs, which can present smaller than 1 mm. However, few of these studies have characterized features of pigmented small‐sized BCC. The application of in‐vivo imaging such as dermoscopy and reflectance confocal microscopy (RCM) allows for the non‐invasive distinction of these lesions from benign and malignant melanocytic neoplasms, thereby reducing unnecessary biopsies. Methods: Within one year, three patients presented to Oregon Health and Science University's dermatology clinic with pigmented lesions of concern measuring less than 2 mm that were histologically confirmed as pigmented BCC. We sought to characterize the features of these lesions in a case series with the non‐invasive imaging modalities of dermoscopy and RCM. Results: All cases presented clinically as a small, brown, macule on the face. Each of the three cases exhibited differing features on dermoscopy. With the application of RCM, we were able to visualize characteristic BCC features, prompting removal by shave biopsy. Conclusion: To our knowledge, no other study has reported dermoscopic and RCM features of a cohort of pigmented BCCs 2 mm in diameter or smaller. We propose to define BCCs of this size as micro‐BCCs. The variability of dermoscopic findings observed in our study, combined with the small size of these pigmented lesions, shows the utility of RCM as a non‐invasive diagnostic tool for pigmented micro‐BCCs. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

The performance of deep networks for medical image analysis is often constrained by limited medical data, which is privacy-sensitive. Federated learning (FL) alleviates the constraint by allowing different institutions to collaboratively train a federated model without sharing data. However, the federated model is often suboptimal with respect to the characteristics of each client's local data. Instead of training a single global model, we propose Customized FL (CusFL), for which each client iteratively trains a client-specific/private model based on a federated global model aggregated from all private models trained in the immediate previous iteration. Two overarching strategies employed by CusFL lead to its superior performance: 1) the federated model is mainly for feature alignment and thus only consists of feature extraction layers; 2) the federated feature extractor is used to guide the training of each private model. In that way, CusFL allows each client to selectively learn useful knowledge from the federated model to improve its personalized model. We evaluated CusFL on multi-source medical image datasets for the identification of clinically significant prostate cancer and the classification of skin lesions. [ABSTRACT FROM AUTHOR]

Background: In areas without convenient access to dermatology care, primary care providers (PCPs) serve as an important patient resource for early skin cancer detection. To determine the most effective strategy for skin cancer detection training in PCPs, we conducted a systematic review of educational interventions and performed a meta-analysis on sensitivity and specificity outcomes in PCPs.Objectives: To summarize data on skin cancer sensitivity and specificity outcomes for PCP-targeted training programs and diagnostic algorithms. Our PCP cohort included practicing physicians, trainee physicians, and advanced practice practitioners.Methods: A literature search was performed in MEDLINE, Embase, Web of Science, and the Cochrane Library for relevant English-language articles published worldwide from 2000 onward. Results were screened for eligibility, and overlapping datasets were reconciled. Data extracted included the educational intervention, diagnostic algorithm, and outcomes of interest (sensitivity and specificity). Outcomes were pooled across interventions that taught the same diagnostic algorithm. A bivariate model was fit to compare different interventions/algorithms. This review followed the PRISMA guidelines.Results: In total, 21 articles were included in this review, encompassing over 58,610 assessments of skin lesions by about 1529 participants worldwide. Training programs that implemented the triage-amalgamated dermoscopic algorithm (TADA) demonstrated high pooled sensitivity (91.7%) and high pooled specificity (81.4%) among PCPs.Conclusions and Relevance: Overall, this systematic review and meta-analysis showed that dermoscopy training in PCPs was generally associated with gains in skin cancer sensitivity without loss of specificity. Clinically, this correlates with fewer skin cancers overlooked by PCPs and fewer excisions of benign lesions. [ABSTRACT FROM AUTHOR]

Skin cancer, particularly melanoma, has been recognized as one of the most lethal forms of cancer. Detecting and diagnosing skin lesions accurately can be challenging due to the striking similarities between the various types of skin lesions, such as melanoma and nevi, especially when examining the color images of the skin. However, early diagnosis plays a crucial role in saving lives and reducing the burden on medical resources. Consequently, the development of a robust autonomous system for skin cancer classification becomes imperative. Convolutional neural networks (CNNs) have been widely employed over the past decade to automate cancer diagnosis. Nonetheless, the emergence of the Vision Transformer (ViT) has recently gained a considerable level of popularity in the field and has emerged as a competitive alternative to CNNs. In light of this, the present study proposed an alternative method based on the off-the-shelf ViT for identifying various skin cancer diseases. To evaluate its performance, the proposed method was compared with 11 CNN-based transfer learning methods that have been known to outperform other deep learning techniques that are currently in use. Furthermore, this study addresses the issue of class imbalance within the dataset, a common challenge in skin cancer classification. In addressing this concern, the proposed study leverages the vision transformer and the CNN-based transfer learning models to classify seven distinct types of skin cancers. Through our investigation, we have found that the employment of pre-trained vision transformers achieved an impressive accuracy of 92.14%, surpassing CNN-based transfer learning models across several evaluation metrics for skin cancer diagnosis.

Skin cancer is the cancer type with the highest incidence in the world. Its diagnosis requires a specialist physician, with expertise in skin diagnostics. Thermography is a noninvasive technique based on the detection of infrared emission that is completely safe to humans. In this study, thermal images of clinically similar lesions were registered and analyzed aiming to provide a noninvasive diagnostic information for discrimination of: basal cell carcinoma versus intradermal nevus, squamous cell carcinoma versus actinic keratosis, and melanoma versus pigmented seborrheic keratosis. Thermal images were analyzed using a MATLAB® routine to evaluate statistical, histogram, and filtering metrics of each image, and a support vector machine classifier was used to discriminate the lesions based on those metrics values. Actinic keratoses and squamous cell carcinoma showed distinct average temperatures, whereas the other pairs of lesions presented similar temperatures. Nevertheless, the benign lesions showed higher definition of borders detection than malignant lesions, as a general rule. The results showed that support vector machine classifier has great ability for discrimination of clinically similar lesions based on their thermal images, suggesting that the thermography can be used as an auxiliary tool for the diagnosis of skin malignant lesions. [ABSTRACT FROM AUTHOR]

An automated system for skin cancer diagnosis is needed for early diagnosis to reduce the mortality rate of skin cancer. Noninvasive clinical routines are essential for diagnosis, but they are largely subjective. In this study, a hybrid deep learning (HDL) approach that uses subband fusion of 3D wavelets is proposed. It is a noninvasive and objective method for inspecting skin images. In the first stage of the HDL approach, simple median filtering is used to remove unwanted information such as hair and noise. In the second stage, the 3D wavelet transform is applied to obtain textural information from the dermoscopic image via a subband fusion approach. In the final stage, multiclass classification is performed by the HDL approach using the fused subband. The performance results of the HDL approach on PH2 database images indicate that it can discriminate normal, benign, and malignant skin images effectively with 99.33% average accuracy and more than 90% sensitivity and specificity. This study confirms the observation that the HDL approach can realize improved classification results. [ABSTRACT FROM AUTHOR]

Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their earliest stages using dermoscopic imaging. Computer-aided diagnosis (CAD) methods relying on deep learning techniques especially convolutional neural networks (CNN) can effectively address this issue with outstanding outcomes. Nevertheless, such black box methodologies lead to a deficiency in confidence as dermatologists are incapable of comprehending and verifying the predictions that were made by these models. This article presents an advanced an explainable artificial intelligence (XAI) based CAD system named "Skin-CAD" which is utilized for the classification of dermoscopic photographs of SC. The system accurately categorises the photographs into two categories: benign or malignant, and further classifies them into seven subclasses of SC. Skin-CAD employs four CNNs of different topologies and deep layers. It gathers features out of a pair of deep layers of every CNN, particularly the final pooling and fully connected layers, rather than merely depending on attributes from a single deep layer. Skin-CAD applies the principal component analysis (PCA) dimensionality reduction approach to minimise the dimensions of pooling layer features. This also reduces the complexity of the training procedure compared to using deep features from a CNN that has a substantial size. Furthermore, it combines the reduced pooling features with the fully connected features of each CNN. Additionally, Skin-CAD integrates the dual-layer features of the four CNNs instead of entirely depending on the features of a single CNN architecture. In the end, it utilizes a feature selection step to determine the most important deep attributes. This helps to decrease the general size of the feature set and streamline the classification process. Predictions are analysed in more depth using the local interpretable model-agnostic explanations (LIME) approach. This method is used to create visual interpretations that align with an already existing viewpoint and adhere to recommended standards for general clarifications. Two benchmark datasets are employed to validate the efficiency of Skin-CAD which are the Skin Cancer: Malignant vs. Benign and HAM10000 datasets. The maximum accuracy achieved using Skin-CAD is 97.2 % and 96.5 % for the Skin Cancer: Malignant vs. Benign and HAM10000 datasets respectively. The findings of Skin-CAD demonstrate its potential to assist professional dermatologists in detecting and classifying SC precisely and quickly., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light–tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device.

Background: Due to the COVID-19 pandemic, teledermoscopy has been increasingly used in the remote diagnosis of skin cancers. In a study conducted in 2020, we demonstrated a potential role of an inexpensive device (NurugoTM Derma) as a first triage to select the skin lesions that require a face-to-face consultation with dermatologists. Herein, we report the results of a novel study that aimed to better investigate the performance of NurugoTM. Objectives: (i) verify whether the NurugoTM can be a communication tool between the general practitioner (GP) and dermatologist in the first assessment of skin lesions, (ii) analyze the degree of diagnostic–therapeutic agreement between dermatologists, (iii) estimate the number of potentially serious diagnostic errors. Methods: One hundred and forty-four images of skin lesions were collected at the Dermatology Outpatient Clinic in Novara using a conventional dermatoscope (instrument F), the NurugoTM (instrument N), and the latter with the interposition of a laboratory slide (instrument V). The images were evaluated in-blind by four dermatologists, and each was asked to make a diagnosis and to specify a possible treatment. Results: Our data show that F gave higher agreement values for all dermatologists, concerning the real clinical diagnosis. Nevertheless, a medium/moderate agreement value was obtained also for N and V instruments and that can be considered encouraging and indicate that all examined tools can potentially be used for the first screening of skin lesions. The total amount of misclassified lesions was limited (especially with the V tool), with up to nine malignant lesions wrongly classified as benign. Conclusions: NurugoTM, with adequate training, can be used to build a specific support network between GP and dermatologist or between dermatologists. Furthermore, its use could be extended to the diagnosis and follow-up of other skin diseases, especially for frail patients in emergencies, such as the current pandemic context. [ABSTRACT FROM AUTHOR]

We present the update of the recommendations of the French society of oncological radiotherapy on radiotherapy of cutaneous cancers. The indications of radiotherapy for skin cancers are not clearly defined because of the lack of randomized trials or prospective studies. For basal cell carcinomas, radiotherapy frequently offers a good local control, but a randomized trial showed that surgery is more efficient and less toxic. Indications of radiotherapy are contra-indications of surgery for patients older than 60, non-sclerodermiform histology and located in non-sensitive areas. Adjuvant radiotherapy could be proposed to squamous cell carcinomas, in case of poor prognostic factors. Dose of 60 to 70 Gy are usually required, and must be modulated to the size of the lesions. Adjuvant radiotherapy seems beneficial for desmoplastic melanomas but not for the other histological types. Prophylactic nodal irradiation (45 to 50 Gy), for locally advanced tumors (massive nodal involvement), decreases the locoregional failure rate but do not increase survival. Adjuvant radio- therapy (50 to 56 Gy) for Merkel cell carcinomas increases also the local control rate, as demonstrated by meta-analysis and a large epidemiological study. Nodal areas must be included, if there is no surgical exploration (sentinel lymph node dissection). Kaposi sarcomas are radiosensitive and could be treated with relatively low doses (24 to 30 Gy). Also, cutaneous lymphomas are good indications for radiotherapy: B lymphomas are electively treated with limited fields. The role of total skin electron therapy for T-lymphomas is still discussed; but palliative radiotherapy is very efficient in case of cutaneous nodules. [ABSTRACT FROM AUTHOR]

Skin cancer is one of themost severe diseases, andmedical imaging is among themain tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and Inception-V3 are proposed and compared using the HAM10000 dataset. The results obtained by the three models demonstrate accuracies of 98%, 97%, and 96%, respectively. Finally, the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%. The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease. [ABSTRACT FROM AUTHOR]

The skin, which has seven layers, is the main human organ and external barrier. According to the World Health Organization (WHO), skin cancer is the fourth leading cause of non-fatal disease risk. In medicinal fields, skin disease classification is a major challenging issue due to inaccurate outputs, overfitting, larger computational cost, and so on. We presented a novel approach of support vector machine-based black widow optimization (SVM-BWO) for skin disease classification. Five different kinds of skin disease images are taken such as psoriasis, paederus, herpes, melanoma, and benign with healthy images which are chosen for this work. The pre-processing step is handled to remove the noises from the original input images. Thereafter, the novel fuzzy set segmentation algorithm subsequently segments the skin lesion region. From this, the color, gray-level co-occurrence matrix texture, and shape features are extracted for further process. Skin disease is classified with the usage of the SVM-BWO algorithm. The implementation works are handled in MATLAB-2018a, thereby the dataset images were collected from ISIC-2018 datasets. Experimentally, various kinds of performance analyses with state-of-the-art techniques are performed. Anyway, the proposed methodology outperforms better classification accuracy of 92% than other methods. Workflow diagram of the proposed methodology. [ABSTRACT FROM AUTHOR]

Management of post-operative soft-tissue defects on the lower legs is challenging owing to arterial and venous insufficiency, poor skin quality including epidermal and dermal atrophy, insufficient tissue laxity, and increased risk of infection. This paper highlights the management of post-operative soft-tissue defects on the lower extremity that cannot be closed primarily or by reconstruction with a local flap. A systematic review of the literature was performed using the National Library of Medicine (NLM) PubMed online database. Articles were included if they reported the management of post-operative lower extremity soft-tissue defects with secondary intention healing, full-thickness skin graft, splitthickness skin grafts, or skin substitutes. Sixty-three articles were included for analysis. There are several options for managing surgical defects on the lower legs and the method chosen should depend on various factors, including the quality of the skin, vascularity and size of the defect, medical history of the patient, and the experience of the surgeon. [ABSTRACT FROM AUTHOR]

Introduction: Dermoscopy is one of the most commonly used methods in early diagnosis of melanoma. It is conducted to differentiate between benign and malignant patterns in melanocytic lesions. Aim: To determine if there is a predominance of one dermoscopic pattern in patients with melanoma and if there is a significant difference in dominant global dermoscopic pattern in patients with cutaneous melanomas correlated with patients' sex and the location of the primary tumor. Material and methods: The study included 162 patients with prior diagnosis of cutaneous melanoma. Dermoscopic and videodermoscopic pictures and patient data were analyzed with regard to the pattern: reticular, globular, homogeneous and mixed pattern (two-component pattern; reticular-globular pattern) with central or peripheral globules and multicomponent (mixed -- at least 3 types of structures in one nevus). Results: The reticular pattern was significantly more prevalent in male patients (38.57%, 27 patients) in comparison to female patients (18.45%, 17 patients). We also found a statistically significant lower prevalence of reticular pattern in patients diagnosed with melanomas located on upper limbs. The homogeneous pattern was statistically significantly more prevalent in patients in whom primary tumors were located on the head and upper limbs. Conclusions: Our study suggests that predominant complex patterns are more commonly observed in patients diagnosed with cutaneous melanoma, although there is a significant number of patients with predominant reticular and homogeneous patterns, which are not often associated with an increased risk of development of melanoma. [ABSTRACT FROM AUTHOR]

Introduction: Regular skin self-examination (SSE) is very important for the early diagnosis of malignant melanoma (MM). Since sun exposure is the most important trigger factor for the development of skin cancers, effective and regular sun protection is the main preventive method. Aim: To investigate the awareness of SSE, risky nevus and sun protection of the adolescents in the Turkish Republic of Northern Cyprus (TRNC). Material and methods: The data used within this research were obtained from questionnaires administered to volunteer high school students in the TRNC. Results: 39.8% of the participants included in the study stated that they conducted SSE. All the participants who said they conducted SSE reported that they performed a face exam. The body regions that the participants did not examine were the scalp (47.7%), foot (36.9%), back (35.4%) and genital area (35.4%). The features of the nevus perceived by the participants as risk factors included rapid growth, bleeding and itching. While 74% of respondents said they used sunscreen products, only 9% of users reported using them every day. Conclusions: In the TRNC where the exposure to the sun is high, young people perform skin examinations at high rates in order to monitor their nevi. Nevertheless, the results of this research show that hard-to-reach areas are neglected. [ABSTRACT FROM AUTHOR]

Introduction: Convolutional neural networks gained popularity due to their ability to detect and classify objects in images and videos. It gives also an opportunity to use them for medical tasks in such specialties like dermatology, radiology or ophthalmology. The aim of this study was to investigate the ability of convolutional neural networks to classify malignant melanoma in dermoscopy images. Aim: To examine the usefulness of deep learning models in malignant melanoma detection based on dermoscopy images. Material and methods: Four convolutional neural networks were trained on open source dataset containing dermoscopy images of seven types of skin lesions. To evaluate the performance of artificial neural networks, the precision, sensitivity. F1 score, specificity and area under the receive operationg curve were calculated. In addition, an ensemble of all neural networks' ability of proper malignant melanoma classification was compared with the results achieved by every single network. Results: The best convolutional neural network achieved on average 0.88 precision, 0.83 sensitivity, 0.85 F1 score and 0.99 specificity in the classification of all skin lesion types. Conclusions: Artificial neural networks might be helpful in malignant melanoma detection in dermoscopy images. [ABSTRACT FROM AUTHOR]

Vulvar intraepithelial lesions are a heterogenic group of diseases, which can be easily misdiagnosed. The case of a 61-year-old woman with a history of genital intraepithelial lesions and infection with HPV is presented. Her main complaint was vulvar pruitus. Vulvoscopy revealed the presence of two skin lesions: the first one had the morphology of lichen sclerosus, and the second of a Bowenoid lesion. The biopsy of the first lesion revealed vulvar intraepithelial neoplasia, whereas cells of squamous vulvar cancer were identified in the secon lesion. After staging, the patient was advised to undergo hemivulvectomy and lymphadenectomy. The coexistence of morphologically diverse vulvar skin lesions may cause difficulties with diagnosis and the selection of an adequate treatment. Long-term follow-up and regular examination are essential for diagnosis of vulvar malignancies in the early stage. [ABSTRACT FROM AUTHOR]

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Background: The SARS-CoV-2/COVID-19 pandemic dramatically impacted the delivery of healthcare, including dermatological services in the initial stages of the pandemic reduced patient flow produced a dramatic drop in the volume of skin cancer screening Consistent with COVID-19 precautions, our practice conducted visual skin examinations (VSE) utilizing semi-automated total body photography (TBP). Methods: A cross-sectional study of patient characteristics and self-reported melanoma risk factors associated with TBP usage was conducted on all patients from May to November 2020 in a single practitioner private dermatology setting. The process and histopathology-confirmed outcomes were compared to those in the same 6-month period in 2019. Results For the May-November 2020 timeframe. those who opted for the home TBP (35%) compared to clinic TBP were younger, had higher self-reported skin cancer risk, and were more likely to have had previous TBP sessions. Overall, the number of TBP sessions increased, while dermoscopy usage and biopsy number decreased. There was no change in the number and distribution of skin cancer diagnoses compare to the same period in 2019. The Melanoma-In-Situ:invasive Melanoma (MISINV) ratio was above the US. ratio reported for 2020 of 0.95:1 (95,710 MIS:100,350 INV). Conclusion: Semi-automated TBP was successfully implemented during the pandemic without affecting skin cancer detection. [ABSTRACT FROM AUTHOR]

Background: Due to the COVID-19 pandemic, teledermoscopy has been increasingly used in the remote diagnosis of skin cancers. In a study conducted in 2020, we demonstrated a potential role of an inexpensive device (NurugoTM Derma) as a first triage to select the skin lesions that require a face-to-face consultation with dermatologists. Herein, we report the results of a novel study that aimed to better investigate the performance of NurugoTM. Objectives: (i) verify whether the NurugoTM can be a communication tool between the general practitioner (GP) and dermatologist in the first assessment of skin lesions, (ii) analyze the degree of diagnostic–therapeutic agreement between dermatologists, (iii) estimate the number of potentially serious diagnostic errors. Methods: One hundred and forty-four images of skin lesions were collected at the Dermatology Outpatient Clinic in Novara using a conventional dermatoscope (instrument F), the NurugoTM (instrument N), and the latter with the interposition of a laboratory slide (instrument V). The images were evaluated in-blind by four dermatologists, and each was asked to make a diagnosis and to specify a possible treatment. Results: Our data show that F gave higher agreement values for all dermatologists, concerning the real clinical diagnosis. Nevertheless, a medium/moderate agreement value was obtained also for N and V instruments and that can be considered encouraging and indicate that all examined tools can potentially be used for the first screening of skin lesions. The total amount of misclassified lesions was limited (especially with the V tool), with up to nine malignant lesions wrongly classified as benign. Conclusions: NurugoTM, with adequate training, can be used to build a specific support network between GP and dermatologist or between dermatologists. Furthermore, its use could be extended to the diagnosis and follow-up of other skin diseases, especially for frail patients in emergencies, such as the current pandemic context.

Skin cancer diagnosis often relies on image segmentation as a crucial aid, and a high-performance segmentation can lower misdiagnosis risks. Part of the medical devices often have limited computing power for deploying image segmentation algorithms. However, existing high-performance algorithms for image segmentation primarily rely on computationally intensive large models, making it challenging to meet the lightweight deployment requirement of medical devices. State-of-the-art lightweight models are not able to capture both local and global feature information of lesion edges due to their model structures, result in pixel loss of lesion edge. To tackle this problem, we propose LeaNet, a novel U-shaped network for high-performance yet lightweight skin cancer image segmentation. Specifically, LeaNet employs multiple attention blocks in a lightweight symmetric U-shaped design. Each blocks contains a dilated efficient channel attention (DECA) module for global and local contour information and an inverted external attention (IEA) module to improve information correlation between data samples. Additionally, LeaNet uses an attention bridge (AB) module to connect the left and right sides of the U-shaped architecture, thereby enhancing the model's multi-level feature extraction capability. We tested our model on ISIC2017 and ISIC2018 datasets. Compared with large models like ResUNet, LeaNet improved the ACC, SEN, and SPEC metrics by 1.09 %, 2.58 %, and 1.6 %, respectively, while reducing the model's parameter number and computational complexity by 570x and 1182x. Compared with lightweight models like MALUNet, LeaNet achieved improvements of 2.07 %, 4.26 %, and 3.11 % in ACC, SEN, and SPEC, respectively, reducing the parameter number and computational complexity by 1.54x and 1.04x., Competing Interests: Declaration of competing interest All authors disclosed no relevant relationships., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: As skin cancer is the most common cancer in the U.S., many publications focus on cutaneous malignancies. The objective was to identify and analyze the 100 most-cited articles pertaining to skin cancer to provide clarity on past, and insight for future research. Methods: Using a two-stage search of Thomson Reuters Web of Science database, the 100 most-cited skin cancer articles between 1945 and 2018 were identified and analyzed. Results: The articles were cited an average total of 558.49 times (range: 259 to 3429). There were relatively few significant publications until 1988. Linear correlation revealed that journal impact factor was not correlated with average number of citations per year per article (R2=0.06). Molecular pathogenesis and UV exposure were the most common topics. The topics that received the highest average citations per year per article were prevention (50.44 citations per year), diagnosis (47.32 citations per year), and management (41.37 citations per year). Basic science articles accounted for the most publications, followed by narrative literature reviews. Conclusions: These results may indicate that future publications with similar characteristics are more likely to be cited and thus, more influential. [ABSTRACT FROM AUTHOR]

Medical and many other applications require small-volume setups enabling terahertz imaging. Therefore, we aim to develop a device for the in-reflection examination of the samples. Thus, in this article, we focus on the diffractive elements for efficient redirection and focusing of the THz radiation. A terahertz diffractive optical structure has been designed, optimized, manufactured (using extrusion-based 3D printing) and tested. Two manufacturing methods have been used—direct printing of the structures from PA12, and casting of the paraffin structures out of 3D-printed molds. Also, the limitations of the off-axis focusing have been discussed. To increase the efficiency, an iterative algorithm has been proposed that optimizes off-axis structures to focus the radiation into small focal spots located far from the optical axis, at an angle of more than 30 degrees. Moreover, the application of higher-order kinoform structure design allowed the maintaining of the smallest details of the manufactured optical element, using 3D printing techniques. [ABSTRACT FROM AUTHOR]

Machine learning models have been successfully applied for analysis of skin images. However, due to the black box nature of such deep learning models, it is difficult to understand their underlying reasoning. This prevents a human from validating whether the model is right for the right reasons. Spurious correlations and other biases in data can cause a model to base its predictions on such artefacts rather than on the true relevant information. These learned shortcuts can in turn cause incorrect performance estimates and can result in unexpected outcomes when the model is applied in clinical practice. This study presents a method to detect and quantify this shortcut learning in trained classifiers for skin cancer diagnosis, since it is known that dermoscopy images can contain artefacts. Specifically, we train a standard VGG16-based skin cancer classifier on the public ISIC dataset, for which colour calibration charts (elliptical, coloured patches) occur only in benign images and not in malignant ones. Our methodology artificially inserts those patches and uses inpainting to automatically remove patches from images to assess the changes in predictions. We find that our standard classifier partly bases its predictions of benign images on the presence of such a coloured patch. More importantly, by artificially inserting coloured patches into malignant images, we show that shortcut learning results in a significant increase in misdiagnoses, making the classifier unreliable when used in clinical practice. With our results, we, therefore, want to increase awareness of the risks of using black box machine learning models trained on potentially biased datasets. Finally, we present a model-agnostic method to neutralise shortcut learning by removing the bias in the training dataset by exchanging coloured patches with benign skin tissue using image inpainting and re-training the classifier on this de-biased dataset.

Skin cancer is the most common cancer worldwide, with rapidly increasing incidence and consistent mortality. Skin cancer encompasses melanoma and non-melanoma skin cancer, which in turn is mainly divided into cutaneous squamous cell carcinoma and basal cell carcinoma. Small noncoding micro-RNAs (miRNAs) regulate protein expression after transcription and play a role in the development and progression of skin cancer. Deregulated expression of miRNAs in skin cancer is associated with cell proliferation, angiogenesis, metastasis, apoptosis, immune response, and drug resistance. Specific patterns of miRNAs in specific skin cancer types can be used as diagnostic markers. For therapeutic purposes, both miRNA and chemically modified variants thereof as well as miRNA antagonists (antagomiRs) or RNA inhibitors may be applied topically. Due to their specific physicochemical properties, physical or chemical diffusion promoters are used with varying degrees of success. There is no question by now that such preparations have a high potential for the treatment of epithelial skin tumors in particular. [ABSTRACT FROM AUTHOR]

Background/Aim: Peritumoral clefting is one of the main histologic features of basal cell carcinoma of the skin (BCC). The aim of the study was to analyze the expression of MMP-2 and MMP-9 both in cells of basal cell carcinoma and in the adjacent stroma and to correlate the findings of immunohistochemical analysis with the presence of peritumoral clefting. Patients and Methods: The study was made on archival material comprising 48 cases of BCC. These were scanned for the presence of peritumoral clefts. The results of immunohistochemical staining for MMP-2 and MMP-9 were determined semiquantitatively using immunohistochemical staining index (ISI). Results: Peritumoral retractions were found in 40 BCC cases. Positive immunohistochemical reaction for MMP-2 in tumor cells was found in 47 cases and in all cases in the adjacent stroma. Positive immunostaining for MMP-9 in BCC tumor cells was observed in 37 cases and in all cases in the adjacent stroma. There was no statistically significant association between peritumoral retractions and expression of MMPs. A statistically significant correlation was found in the expression of both MMP-2 and MMP-9 between the tumor and the stroma. Conclusion: Tumor cells elaborate MMP-2 and -9, but they also produce some other factors that may induce production of MMPs in adjacent stromal cells. The role of MMPs in the development of peritumoral clefts could not be confirmed. [ABSTRACT FROM AUTHOR]

There is a pressing need for monitoring cancerous tissue response to laser therapy. In this work, we evaluate the viability of elastic scattering spectroscopy (ESS) to monitor malignant transformations and effects of laser therapy of induced skin cancer in a hamster model. Skin tumors were induced in 35 mice, half of which were irradiated with 980 nm laser diode. Physiological and morphological transformations in the tumor were monitored over a period of 36 weeks using elastic scattering spectroscopy, in the near infrared window. Analytical model for light scattering was used to derive scattering optical properties for both transformed tissue and laser-treated cancer. The tissue scattering over the wavelength range (700-950 nm) decreased remarkably as the carcinogen-induced tissue transformed towards higher stages. Conversely, reduced scattering coefficient noticeably increased with increasing the number of laser irradiation sessions for the treated tumors. The relative changes in elastic scattering signal for transformed tissue were significantly different (p < .05). Elastic scattering signal intensity for laser-treated tissue was also significantly different (p < .05). Reduced scattering coefficient of treated tissue exhibited nearly 80% recovery of its normal skin value at the end of the experiment, and the treatment outcome could be improved by adjusting the number of sessions, which we can predict through spectroscopic optical feedback. This study demonstrates that ESS can quantitatively provide functional information that closely corresponds to the degree of pathologic transformation. ESS may well be a viable technique to optimize systemic melanoma and non-melanoma skin cancer treatment based on noninvasive tumor response. [ABSTRACT FROM AUTHOR]