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The accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet. [ABSTRACT FROM AUTHOR]

Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score. [ABSTRACT FROM AUTHOR]

Breast cancer classification and segmentation play an important role in identifying and detecting benign and malignant breast lesions. However, segmentation and classification still face many challenges: 1) The characteristics of cancer itself, such as fuzzy edges, complex backgrounds, and significant changes in size, shape, and intensity distribution make accurate segment and classification challenges. 2) Existing methods ignore the potential relationship between classification and segmentation tasks, due to the classification and segmentation being treated as two separate tasks. To overcome these challenges, in this paper, a novel Semantic‐aware transformer (SaTransformer) for breast cancer classification and segmentation is proposed. Specifically, the SaTransformer enables doing the two takes simultaneously through one unified framework. Unlike existing well‐known methods, the segmentation and classification information are semantically interactive, reinforcing each other during feature representation learning and improving the ability of feature representation learning while consuming less memory and computational complexity. The SaTransformer is validated on two publicly available breast cancer datasets – BUSI and UDIAT. Experimental results and quantitative evaluations (accuracy: 97.97%, precision: 98.20%, DSC: 86.34%) demonstrate that the SaTransformer outperforms other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

AbstractBreast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG‐16, are utilised and re‐trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the support vector machine (SVM) classifier is utilised instead of the last fully connected layer. Our models performance are verified utilising k‐fold cross‐validation. Our proposed techniques are trained and evaluated on three mammographic datasets: mammographic image analysis society, digital database for screening mammography (DDSM) and the curated breast imaging subset of DDSM. This paper explains end‐to‐end fully convolutional neural networks without any prepossessing or post‐processing. The proposed technique of employing ResNet50 hybridised with SVM achieves the best performance, specifically with the DDSM dataset, producing 97.98% accuracy, 98.46% area under the curve, 97.63% sensitivity, 96.51% precision, 95.97% F1 score and computational time 1.8934 s. [ABSTRACT FROM AUTHOR]

A mammogram is an image of a breast used to detect and diagnose breast cancer. This paper emphases a Computer‐Aided Detection system based on convolutional neural network (CNN) that uses the concept of deep learning to classify the mammogram images into benign, malignant and normal. The proposed CNN model consists of eight convolutional, four max‐pooling and two fully connected layers and achieved better results compared to the pre‐trained nets, AlexNet and VGG16. The proposed model demonstrates the feasibility of using CNNs on medical image processing techniques for the classification of breast masses. The results are also compared with the state‐of‐the‐art machine learning algorithm like kNN classifier. Experimentation is done with three datasets. Among them, two are publicly available, Mammographic Image Analysis Society (MIAS), digital database for screening mammography (DDSM) and an internally collected dataset. The proposed model achieved accuracies of 92.54, 96.47 and 95 and the Area under the ROC curve (AUC) score of 0.85, 0.96 and 0.94 for MIAS, DDSM and the internally collected dataset respectively. Furthermore, the images of the three datasets are merged to build one large set and used to fine tune the proposed CNN model and produced accuracy of 98.32 and AUC of 0.98. [ABSTRACT FROM AUTHOR]

Melanoma is one of the four major types of skin cancers caused by malignant growth in the melanocyte cells. It is the rarest one, accounting to only 1% of all skin cancer cases. However, it is the deadliest among all the skin cancer types. Owing to its rarity, efficient diagnosis of the disease becomes rather difficult. Here, a deep depthwise separable residual convolutional algorithm is introduced to perform binary melanoma classification on a dermoscopic skin lesion image dataset. Prior to training the model with the dataset noise removal from the images using non‐local means filter is performed followed by enhancement using contrast‐limited adaptive histogram equilisation over discrete wavelet transform algorithm. Images are fed to the model as multi‐channel image matrices with channels chosen across multiple color spaces based on their ability to optimize the performance of the model. Proper lesion detection and classification ability of the model are tested by monitoring the gradient weighted class activation maps and saliency maps, respectively. Dynamic effectiveness of the model is shown through its performance in multiple skin lesion image datasets. The proposed model achieved an ACC of 99.50% on international skin imaging collaboration (ISIC), 96.77% on PH2, 94.44% on DermIS and 95.23% on MED‐NODE datasets. [ABSTRACT FROM AUTHOR]

Red Blood Corpuscles called Erythrocytes are the most important element in blood composition which is mainly responsible in all living cells. To detect the cancer cell various methods are employed. In this paper, proper identification of cancer cells from unaffected RBCs are detected. The proposed novel method called Online Region Based Segmentation (ORBS) method is done that is used to find the regions of corpuscles. By using properties, metric is formulated for determination of shape which is abnormal in blood cells. Overall accuracy of 96.9% is obtained using proposed ORBS method and deep learning classification (DLC) method has accuracy of 97.1% that helps to diagnose cancer cells using feature extraction process done automatically. Sensitivity, specificity and precision values of the proposed segmentation method is found to be 96.7%, 95.6% and 98.4% respectively. The computation time was found as 22 seconds. Closeness of Proposed method in relative to True Positive values at the ROC curve indicates the performance as higher. Comparative analysis is made with ResNet‐50 based on the different testing and training data at rate of 90%−10%, 80%−20% and 70%−30% respectively, which proves the robustness of proposed research work. Experimental results prove proposed system effectiveness compared with other detection methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel method of active contours based on the formulation of partial differential equation (PDE) is proposed for image segmentation. The evolution equation incorporates a force term that pushes the contour towards object boundary, a regularisation term which takes into account the smoothness of the level set function and an edge term which helps to stop the contour at required boundaries. The proposed method integrates an image convolved by a variable kernel into an energy formulation, where the width of the kernel varies in each iteration. Therefore, it takes local region information when the width of the kernel is small while for the larger width of the kernel, the proposed method considers global region information across the regions. Due to the use of both local and global image information, the method easily detects objects in the complex background and also segments the objects where intensity changes within the object. Moreover, the proposed method totally eliminates the need of the contour initialisation by using constant initialisation scheme. Experimental results on real and medical images prove the robustness of the proposed method. Finally, the authors validate their method on PH2 database for skin lesion segmentation. [ABSTRACT FROM AUTHOR]

Skin cancer is common and deadly, needs to be detected and treated properly. Deep learning algorithms like UNet have shown potential results in medical imaging. Such approaches still struggle to capture fine‐grained details and scale differences in skin lesions‐based occlusions' appearance, size etc. This research proposes a redesign UNet, the Multi‐Scale Pyramid Attention Network (MSPAN), to improve skin cancer lesion segmentation. The input data is processed at numerous scales with varied receptive fields. This enhances the network's ability to identify lesion locations by capturing local and global context. Attention approaches also help the network to suppress noise by focusing on informative features. We have evaluated MSPAN model on the publicly available ISIC2018 benchmark dataset for skin lesion segmentation. The method surpasses traditional UNet and other current methods in accuracy and effectiveness. The model also has a post‐processing to estimate lesion area for fast inference, making it suitable for extensive screening. Redesigned UNet with the Multi‐Scale Pyramid Attention Network improves skin cancer lesion segmentation. The model's ability to collect fine‐grained information and handle occlusions allows for more accurate skin cancer diagnosis and treatment. The MSPAN design can improve computer‐aided diagnosis systems and help dermatologists make precise clinical decisions. [ABSTRACT FROM AUTHOR]

The fusion and utilization of multi‐scale deep and shallow features are of great significance in liver tumour segmentation. This study proposes a dual encoding DDS‐UNet liver tumour segmentation method based on multi‐scale deep and shallow feature fusion, aiming to fully achieve the fusion and utilization of deep and shallow features and achieve accurate segmentation. The proposed method mainly consists of residual convolution module fusion residual convolution (FRC), dual encoding end fusion module dual encoding end fusion (DEF), and skip connection fusion module jump connection fusion module (JCF). In the residual convolution module, a layer by layer fused residual convolution is used instead of traditional convolution to achieve better training. In the dual encoding end fusion module, multi‐scale feature fusion at the end provides more comprehensive contextual information, solves the loss of spatial geometric information. The skip connection fusion module reduces the interference of invalid features by changing the weights of spatial attention and channel attention on important features. The Dice coefficient, average intersection to union ratio, accuracy, recall, and accuracy indicators tested on the LiTS dataset were 90.37%, 90.16%, 93.78%, 94.91%, and 98.84%, respectively, which are superior to many advanced liver tumor segmentation methods. [ABSTRACT FROM AUTHOR]

The prediction of lung tumour growth is the key to early treatment of lung cancer. However, the lack of intuitive and clear judgments about the future development of the tumour often leads patients to miss the best treatment opportunities. Combining the characteristics of the variational autoencoder and recurrent neural networks, this study proposes a tumour growth prediction via a conditional recurrent variational autoencoder. The proposed model uses a variational autoencoder to reconstruct tumour images at different times. Meanwhile, the recurrent units are proposed to infer the relationship between tumour images according to the chronological order. The different tumour development varies in different patients, patients' condition is adopted to achieve personalised prediction. To solve the problem of blurred results, the authors add the total variation regularisation term into the object function. The proposed method was tested on longitudinal studies, National Lung Screening Trial and cooperative hospital dataset, with three points on lung tumours. The precision, recall, and dice similarity coefficient reach 82.22, 79.89 and 82.49%, respectively. Both quantitative and qualitative experimental results show that the proposed method can produce realistic tumour images. [ABSTRACT FROM AUTHOR]

Pulmonary nodules risk classification in adenocarcinoma is essential for early detection of lung cancer and clinical treatment decision. Improving the level of early diagnosis and the identification of small lung adenocarcinoma has been always an important topic for imaging studies. In this study, the authors propose a deep convolutional neural network (CNN) with scale‐transfer module (STM) and incorporate multi‐feature fusion operation, named STM‐Net. This network can amplify small targets and adapt to different resolution images. The evaluation data were obtained from the computed tomography (CT) database provided by Zhongshan Hospital Fudan University (ZSDB). All data have a pathological label and their lung adenocarcinomas risk are classified into four categories: atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma. The authors' deep learning network STM‐Net was trained and tested for the risk stage prediction. The accuracy and the average area under the receiver operating characteristic curve achieved by their method are 95.455% and 0.987 for the ZSDB dataset. The experimental results show that STM‐Net largely boosts classification accuracy on the pulmonary nodules classification compared with state‐of‐the‐art approaches. The proposed method will be an effective auxiliary to help physicians diagnosis pulmonary nodules risk classification in adenocarcinoma in early‐stage. [ABSTRACT FROM AUTHOR]

With the increasing incidence rate of lung cancer patients, early diagnosis could help in reducing the mortality rate. However, accurate recognition of cancerous lesions is immensely challenging owing to factors such as low contrast variation, heterogeneity and visual similarity between benign and malignant nodules. Deep learning techniques have been very effective in performing natural image segmentation with robustness to previously unseen situations, reasonable scale invariance and the ability to detect even minute differences. However, they usually fail to learn domain‐specific features due to the limited amount of available data and domain agnostic nature of these techniques. This work presents an ensemble framework Deep3DSCan for lung cancer segmentation and classification. The deep 3D segmentation network generates the 3D volume of interest from computed tomography scans of patients. The deep features and handcrafted descriptors are extracted using a fine‐tuned residual network and morphological techniques, respectively. Finally, the fused features are used for cancer classification. The experiments were conducted on the publicly available LUNA16 dataset. For the segmentation, the authors achieved an accuracy of 0.927, significant improvement over the template matching technique, which had achieved an accuracy of 0.927. For the detection, previous state‐of‐the‐art is 0.866, while ours is 0.883. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid‐based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial‐aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively. [ABSTRACT FROM AUTHOR]

Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%. [ABSTRACT FROM AUTHOR]

Prediction methods of glioblastoma tumours growth constitute a hard task due to the lack of medical data, which is mostly related to the patients' privacy, the cost of collecting a large medical data set, and the availability of related notations by experts. In this study, the authors propose a synthetic medical image generator (SMIG) with the purpose of generating synthetic data based on the generative adversarial network in order to provide anonymised data. In addition, to predict the glioblastoma multiform tumour growth the authors developed a tumour growth predictor based on end to end convolution neural network architecture that allows training on a public data set from the cancer imaging archive (TCIA), combined with the generated synthetic data. The authors also highlighted the impact of implicating a synthetic data generated using SMIG as a data augmentation tool. Despite small data size provided by TCIA data set, the obtained results demonstrate valuable tumour growth prediction accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre‐processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre‐processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern‐based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern‐based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto‐encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K‐fold. [ABSTRACT FROM AUTHOR]

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer‐free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi‐level thresholding based on Rényi's two‐dimensional entropy with a cultural algorithm (2DReCA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey‐level co‐occurrence matrix and block‐wise elliptical local binary pattern) are calculated based on pre‐processed grey‐scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques. [ABSTRACT FROM AUTHOR]

A chronic disorder caused by abnormal growth of the lung cells in the pulmonary tumour. This study suggests a modern automated approach to improve efficiency and decrease the difficulty of lung tumour diagnosis. The proposed algorithm for lung tumour sensing consists of four phases: pre‐processing, segmentation, extraction, and characteristics classification. The first stage is the image acquisition here input lung image is read and then resized. The second stage is the image pre‐processing here Perona–Malik diffusion with unsharp masking filter is proposed for enhancement purposes. The third stage is the segmentation here the improved histogram–based fast 2D Otsu's thresholding is proposed for lung tumour segmentation purposes. Finally, linear discriminant analysis classifier, support vector machine (SVM) classifier, SVM–sequence minimal optimisation classifier, Naive Bayes classifier, SVM–advance sequence minimal optimisation (SVM–ASMO) classification [proposed] included in the various classifier groups adopted in this report. Overall performance accuracy of 0.962 is obtained using the proposed SVM–ASMO method that helps to diagnose the cancer cells using the feature extraction process, which is done automatically. The specificity, precision, recall, and F1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984,respectively. [ABSTRACT FROM AUTHOR]

Brain tumour segmentation is the process of separating the tumour from normal brain tissues. A glioma is a kind of tumour, which fires up in the glial cells of the spine or the brain. This study introduces a technique for classifying the severity levels of glioma tumour using a novel segmentation algorithm, named DeepJoint segmentation and the multi‐classifier. Initially, the brain images are subjected to pre‐processing and the region of interest is extracted. Then, the segmentation of the pre‐processed image is done using the proposed DeepJoint segmentation, which is developed through the iterative procedure of joining the grid segments. After the segmentation, feature extraction is carried out from core and oedema tumours using information‐theoretic measures. Finally, the classification is done by the deep convolutional neural network (DCNN), which is trained by an optimisation algorithm, named fractional Jaya whale optimiser (FJWO). FJWO is developed by integrating the whale optimisation algorithm in fractional Jaya optimiser. The performance of the proposed FJWO–DCNN with the DeepJoint segmentation method is analysed using accuracy, true positive rate, specificity, and sensitivity. The results depicted that the proposed method produces a maximum accuracy of 96%, which indicates its superiority. [ABSTRACT FROM AUTHOR]

Nowadays, lung cancer is the leading cause of cancer death in both men and women. The early detection of potentially cancerous cells is the best way to improve the patient's chances of survival. In the medical field, computed tomography (CT) is the best imaging technique and it is helpful for doctors to accurately find the cancerous cells. The authors propose an automatic approach to analyse and segment the lungs and classify each lung into normal or cancer. Initially, the CT lung image is pre‐processed to remove noise. Then, they combine the histogram analysis with thresholding and morphological operations to segment and extract the lung regions. In feature extraction stage, the radiomic features of each lung image are extracted separately. Then to improve the classification accuracy, some of the optimum features are selected using modified graph clustering‐based whale optimisation algorithm. Finally, the selected features are classified using ensemble classifiers such as support vector machine, K‐nearest neighbour, and random forest. Experimental result demonstrates that the proposed method achieves better performance in terms of sensitivity, specificity, precision, recall, F‐measure, and accuracy when compared with other state‐of‐art approaches. [ABSTRACT FROM AUTHOR]

With the rapid increase in the cases of deadly skin cancer, the classification on different types of skin cancer has been emerging as one of the most significant issues in the field of medical image. Several approaches have been proposed to help in diagnosing the categories of the skin lesions by means of traditional features or leveraging the widely used deep learning models. However, there are lack of the integrated frameworks to combine the hand‐crafted traditional features and the deep Conv‐features. Furthermore, the effective way to extract global and local features is also conducive to distinguish the specific lesions from normal skin. Hence, in this study, the authors present an integrated model to acquire more representative global–local features including the traditional local binary pattern features and deep Conv‐features. In addition, several fusion strategies have conducted on the Global‐DNN and Local‐DNN for better performance. In order to extract more explicit features from the specific lesion areas, a target segmentation method based on visual saliency detection is employed to eliminate the background interference. Experimental results on ISIC‐2017 skin cancer dataset demonstrate that the proposed Global‐DNN and Global‐Local models can obtain more effective feature representation which achieve outperformed results for skin cancer classification. [ABSTRACT FROM AUTHOR]

Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer‐aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker‐based watershed segmentation. After segmentation, a two‐stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS‐DDSM) database and all the mammographic images that contain masses from the mini‐mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied. [ABSTRACT FROM AUTHOR]

Ultrasound (US) imaging is the leading diagnostic method for assessing the early‐stage thyroid nodule. However, the visual evaluation of nodules can be influenced by the subjectivity of radiologists' interpretations. Computer‐aided Diagnostic (CAD) systems can be useful in classifying these nodules according to their benign or malignant nature. The extraction of the characteristics, which relate in the author's case to the US of thyroid nodules, is essential in the differentiation of these nodules. The complex nature of images, however, generates a significant number of features, many of which are either redundant or irrelevant. This study presents a new CAD system that has been developed to categorise thyroid nodules. In this survey, 447 US images of thyroid nodules were retained. These images were used to extract features using statistical features extraction methods. A feature selection method based on the multi objective particle swarm optimisation algorithm was used to choose the most relevant and non‐redundant ones. Then, support vector machine (SVM) and random forests (RFs) were applied to classify these nodules. 10‐fold cross‐validation was used to assess the classification performance metrics. Their proposed CAD has reached a maximum accuracy of 94.28% for SVM; and 96.13% for RF using the contour‐based ROI. [ABSTRACT FROM AUTHOR]

Automated pancreas segmentation in abdominal computed tomography (CT) scans is of high clinical relevance (i.e. pancreas cancer diagnosis and prognosis), but extremely difficult because the pancreas is a soft, small, and flexible abdominal organ with high anatomical variability, which causes the previous segmentation methods to result in low precision. In this study, the authors present a new deep recurrent adversarial network (DRAN) to tackle this challenge. DRAN contains three steps: (i) preserving global resolution of CT scans and modifying the receptive field of kernel adaptively through a dilated convolution autoencoder module; (ii) modelling contextual spatial correlation between neighbouring CT scan patches benefits from a specially designed local long short‐term memory module; and (iii) improving the performance and generalisation by leveraging an adversarial module, which can constrain the spatial smoothness consistency between continuous CT scans based on the long‐range spatial interaction. The system is evaluated on a dataset of 80 manually segmented CT volumes, using four‐fold cross‐validation. Its performance surpasses other state‐of‐the‐art methods, with the Dice similarity coefficient of 89.87±3.17% and pixel‐wise accuracy of 95.85±3.04%. Also, they perform a qualitative evaluation by an expert further revealing the effectiveness and potential of their DRAN as a clinical segmentation tool. [ABSTRACT FROM AUTHOR]

Lung nodule segmentation is an interesting research topic, and it serves as an effective solution for the diagnosis of Lung cancer. The existing methods of lung nodule segmentation suffer from accuracy issues due to the heterogeneity of the nodules in the lungs and the presence of visual deviations in the nodules. Thus, there is a requirement for an effective lung nodule segmentation, which assists the physicians in making accurate decisions. Accordingly, this study proposes a lung nodule segmentation process based on the kernel‐based Bayesian fuzzy clustering (BFC), which is the integration of kernel functions in the BFC. Initially, the input computed tomography image is pre‐processed for ensuring the effective segmentation, and the lobes are identified using the adaptive thresholding strategy. Then, the dominant areas in the lobes are identified using a scale‐invariant feature transform descriptor, and the significant nodules are extracted using the grid‐based segmentation. Finally, the lung nodules are segmented using the proposed kernel‐based BFC. The proposed algorithm is evaluated using the Lung Image Database Consortium and Image Database Resource Initiative database, and it acquires the accuracy, sensitivity, and false positive rate of 0.955, 0.999, and 0.025, respectively. [ABSTRACT FROM AUTHOR]

Skin lesions can be in malignant or benign forms. Benign skin lesion types are not deadly; however, malignant types of skin lesions can be fatal. Lethal forms are known as skin cancer. These types require urgent clinical treatment. Fast detection and diagnosis of malignant types of skin lesions might prevent life‐threatening scenarios. This work presents two methods for the automatic classification of malignant melanoma and seborrhoeic keratosis lesions. The first method builds on modelling skin images together with wavelet coefficients. Approximate, horizontal, and vertical wavelet coefficients are obtained using the wavelet transform, and then deep learning (DL) models are generated for each of the representations and skin images. The second method builds on modelling skin images together with three approximate coefficients. This method utilises a sequential wavelet transformation to produce approximation coefficients. Then DL models are generated for each of the representations and skin images. Transfer learning‐based ResNet‐18 and ResNet‐50 DL models provide model images and wavelet coefficients. Then skin lesion detection is achieved by fusing model output probabilities. Both proposed models outperform the methods only based on image data and other previously proposed methods. [ABSTRACT FROM AUTHOR]

Brain cancer is a major health problem that affects the lives of many people. Clinicians depend on their medical knowledge to analyse laboratory test results and clinical information extracted manually from medical images, to identify the essential characteristics of tumours such as size, shape, and location. The accurate diagnosis of brain tumours is very essential for deciding the most appropriate treatment protocols and procedures. Apart from being tedious, slow and time‐consuming manual segmentation of brain image scans is also prone to human error. In order to enhance the accuracy and reliability of the diagnosis of brain tumours, rigorous image processing and segmentation is required. In this study the authors present an automated image segmentation method for detecting and localising multiple brain tumours of different sizes and intensities by using a technique of object counting coupled with an evaluation process. The object counting technique were carried out using multiple binary images with different threshold values, and the evaluation process was based on assessment criteria of specific tumour parameters. Different magnetic resonance images with multiple tumours were used for testing the proposed method. The results indicated that the proposed method is effective in detecting and localising multiple true tumours in the brain. [ABSTRACT FROM AUTHOR]

Breast cancer becomes a significant public health problem in the world. During the early detection of breast cancer, it is a very challenging task to classify accurately the benign–malignant patterns in digital mammograms. This study proposes a new fully automated computer‐aided diagnosis (CAD) system for breast cancer diagnosis with high‐accuracy and low‐computational requirements. The expectation–maximisation algorithm is investigated to extract automatically the region of interests (ROIs) within mammograms. The standard shape, statistical, and textural features of ROIs are extracted and combined with multi‐resolution and multi‐orientation features derived from a new feature extraction technique based on wavelet‐based contourlet transform. A hybrid feature selection approach based on combining the support vector machine recursive feature elimination with correlation bias reduction algorithm is proposed. Also, the authors investigate a new similarity‐based learning algorithm, called Q, for benign–malignant classification. The proposed CAD system is applied to real clinical mammograms, and the experimental results demonstrate the superior performance of the proposed CAD system over other existing CAD systems in terms of accuracy 98.16%, sensitivity 98.63%, specificity 97.80%, and computational time 2.2 s. This reveals the effectiveness of the proposed CAD system in improving the accuracy of breast cancer diagnosis in real‐time systems. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to introduce a new species of features to improve the diagnosis efficiency of acute lymphoblastic leukaemia from microscopic images. First, the authors segmented nuclei by the k‐means and watershed algorithms. They extracted three sets of geometrical, statistical, and chaotic features from nuclei images. Six chaotic features were extracted by calculating the fractal dimension from five sub‐images driven from the nuclei images, with their grey levels being modified. The authors classified the images into binary and multiclass types via the support vector machine algorithm. They conducted principal component analysis for dimensional reduction of feature space and then evaluated the proposed algorithm for the overfitting problem. The obtained overall results represent 99% accuracy, 99% specificity, and 97% sensitivity values in the classification of six‐cell groups. The difference between the train and test errors was <3%, which proves that the classification performance had improved by using the multifractal features. [ABSTRACT FROM AUTHOR]

An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre‐processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the leading causes of death in the world. Although early detection of lung tumours (nodules) can remarkably diminish the mortal rate, precise detection of them is not always possible by visual inspection of the computerised tomography images. Since nodules with different sizes have non‐uniform shape and brightness, texture attributes and also the gradient of orientation can be good candidate features, which have been used for this purpose. They determined the co‐occurrence matrix of the extended local binary pattern (ELBP) along with weighted orientation difference (WOD) for each sub‐region of the lung area. Local binary pattern is a texture descriptor that can extract the discriminative features efficiently. The proposed ELBP is rotation invariant and suitable to describe non‐uniform patterns. Moreover, WOD as a structural feature uses the magnitude of each edge as the weight of its orientation difference. After constructing the co‐occurrence matrix, discriminative features were extracted from this matrix and fed into a support vector machine in order to classify each sub‐region as a cancerous (nodule) or normal tissue. The proposed method was compared to some of state‐of‐the‐art nodule detection methods and was assessed over several real datasets in terms of specificity, sensitivity and accuracy. [ABSTRACT FROM AUTHOR]

Pectoral muscle segmentation is a very important step in any Computer Aided Detection (CAD) system, for automatic detection of different possible signs of malignancy in the mammographic image. Presence of high‐density glandular tissues overlapping pectoral muscle boundary, presence of pectoral muscle of small size and low density, presence of pectoralis minor etc., poses a great challenge for researchers working on the problem of automatic pectoral muscle boundary detection. Gabor filter has been used by many researchers to detect textural edges. The ability that it can be tuned along a particular direction, makes it a very important image processing tool. In the proposed research work a Gabor filter tuned in the direction of pectoral muscle boundary is applied on the region of interest (ROI) containing pectoral muscle. Finally, the magnitude and phase response of Gabor filtered ROI image along with an edge‐connect and region merge algorithm is used to detect pectoral muscle boundary. The proposed method is validated using 200 mammograms from the miniMIAS database and 200 mammograms from INbreast database. With an accuracy of 97.50% for miniMIAS database and 94.50% for INbreast database, the proposed method outperforms many state‐of‐the‐art methods which show its suitability for CAD Systems. [ABSTRACT FROM AUTHOR]

Automation‐assisted cervical screening via liquid‐based cytology has achieved great success using segmentation and classification methods. This work tries to do abnormal region detection on field of view cervical cell images based on deep learning, which is a novel way to solve cervical cytological screening problem. Since some abnormal nuclei gather in groups, the proposed method chooses abnormal regions instead of abnormal nuclei as the detection targets in order to locate the abnormal regions for the further diagnosis of the pathologists. In this study, a novel abnormal region detection approach for cervical screening is proposed based on a size‐sensitive fully convolutional network (R‐FCN). Due to the regular feature distribution, a fewer‐layer convolutional neural backbone network is designed for more efficient feature extraction and less running time. In addition, a new measure named hit degree is defined to describe the degree how closely each detected region and the corresponding ground truth matches up. Experimental results show that an average precision of 93.2% is achieved for abnormal region detection in cervical smear images. The proposed method is promising for the development of computer‐aided systems in clinical cervical cytological screening. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the deadliest diseases worldwide and the classification of different types of lung cancers in computed tomography (CT) images is also one of the most significant issues in computer‐aided diagnosis. It remains a tough task since various image features could be extracted from one single image while part of the features is irrelevant to the final diagnosis results. In this study, a knockoff filter‐based approach is proposed to produce the optimal feature set and to minimise the irrelevancy of the output features for the classification of lung cancer in CT images. The proposed feature selection strategy not only can generate the optimal feature subset but also constrain the false discovery rate of the irrelevant features under a specified parameter setting. Ten‐fold leave‐one‐out cross‐validation and the area under the receiver operating characteristic curve are both adopted in the experiments to evaluate the performance of the proposed method. The areas under curve of 0.86±0.02 is achieved when the support vector machine classifier is trained on the features determined by the proposed feature selection strategy. The experimental results demonstrate that the presented approach is potentially valuable for lung cancer diagnosis. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non‐subsampled shearlet transform (NSST) and simplified pulse‐coupled neural network model (S‐PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y‐component of PET image are decomposed into low‐frequency (LF) and high‐frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S‐PCCN which is motivated by an adaptive‐linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency. [ABSTRACT FROM AUTHOR]

Classification of benign and malignant pulmonary nodules can provide useful indicators for estimating the risk of lung cancer. In this study, an improved random forest (RF) algorithm is proposed for classification of benign and malignant pulmonary nodules in thoracic computed tomography images. First, an improved random walk algorithm is proposed to automatically segment pulmonary nodules. Then, intensity, geometric and texture features based on the grey‐level co‐occurrence matrix, rotation invariant uniform local binary pattern and Gabor filter methods are combined to generate an effective and discriminative feature vector. Mutual information is employed to reduce the dimensionality. Finally, an improved RF classifier is trained to classify benign and malignant nodules. An appropriate feature subset is selected by the bootstrap method and an effective combination method is introduced to predict a class label. The proposed classification method on the lung images dataset consortium dataset achieves a sensitivity of 0.92 and the area under the receiver‐operating‐characteristic curve of 0.95. An additional evaluation is performed on another dataset coming from General Hospital of Guangzhou Military Command. A mean sensitivity and a mean specificity of the proposed method are 0.85 and 0.82, respectively. Experimental results demonstrate that the proposed method achieves the satisfactory classification performance. [ABSTRACT FROM AUTHOR]

Automatic identification of abnormal cervical cells, including feature representation, feature combination and classification strategy, is highly demanded in women's annual cervical cancer screenings. However, previous methods only deal with one or two of these three phases, and currently there is few complete framework for this problem. A novel three‐phrase boosting framework is proposed for the detection of abnormal cells from cervical smear images. First, the authors extract 160 dimensional features with respect to each cervical cell from three aspects, including cytology morphology, chromatin pathology and region intensity. In particular, 106 dimensional chromatin pathology features are newly adopted to describe the nucleus textural transformation. Second, an adaptive feature combination method is introduced to select the optimal feature patterns, which can combine all features using a reinforced margin‐based approach with the heuristic knowledge. Finally, a two‐stage classification strategy is presented to reduce erroneous classification abnormal cells using two different classifiers. Experimental results achieve state‐of‐the‐art performance and the proposed framework outperforms the other 16 compared detection methods. [ABSTRACT FROM AUTHOR]

With the development of high‐speed, high‐resolution whole slide histology digital scanners, glass slides of tissue specimen can now be digitised at high magnification to create the whole slide image. Quantitative image analysis tools are then desirable to help the pathologist for their routine examination. Epidermis area is a very important observation area for the cancer diagnosis. Therefore, in order to build up a computer‐aided diagnosis system, segmentation of the epidermis area is often the very first and crucial step. An improved computer‐aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this study. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the response value image. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides superior performance, with sensitivity of 95.68%, specificity of 99.41% and precision of 93.13%. The performance of the proposed technique is satisfactory for future clinical use. [ABSTRACT FROM AUTHOR]