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

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

A deep learning based architecture for multi-class skin cancer classification

Melanoma is considered to be the deadliest variant of skin cancer causing around 75% of total skin cancer deaths. To diagnose Melanoma, clinicians assess and compare multiple skin lesions of the same patient concurrently to gather contextual information regarding the patterns, and abnormality of the skin. So far this concurrent multi-image comparative method has not been explored by existing deep learning-based schemes. In response to this gap, a contextual image feature fusion (CIFF) based deep neural network (CIFF-Net) is proposed, which integrates patient-level contextual information into the traditional approaches for improved Melanoma diagnosis by concurrent multi-image comparative method. The proposed multi-kernel self attention (MKSA) module offers better generalization of the extracted features by introducing multi-kernel operations in the self attention mechanisms. To utilize both self attention and contextual feature-wise attention, an attention guided module named contextual feature fusion (CFF) is proposed that integrates extracted features from different contextual images into a single feature vector. Finally, in comparative contextual feature fusion (CCFF) module, primary and all contextual features are compared concurrently to generate comparative features mirroring the holistic assessment performed by clinicians. Significant improvement in performance has been achieved on the ISIC-2020 dataset over the traditional approaches with scores of 95.7% AUC and 98.3% Accuracy that validate the effectiveness of the proposed contextual learning scheme. 

CIFF-Net: Contextual image feature fusion for Melanoma diagnosis

Melanoma, a prevalent and formidable skin cancer, necessitates early detection for improved survival rates. The rising incidence of melanoma poses significant challenges to healthcare systems worldwide. While deep neural networks offer the potential for precise melanoma classification, the optimization of hyperparameters remains a major obstacle. This paper introduces a groundbreaking approach that harnesses the Manta Rays Foraging Optimizer (MRFO) to empower melanoma classification. MRFO efficiently fine-tunes hyperparameters for a Convolutional Neural Network (CNN) using the ISIC 2019 dataset, which comprises 776 images (438 melanoma, 338 non-melanoma). The proposed cost-effective DenseNet121 model surpasses other optimization methods in various metrics during training, testing, and validation. It achieves an impressive accuracy of 99.26%, an AUC of 99.56%, an F1 score of 0.9091, a precision of 94.06%, and a recall of 87.96%. Comparative analysis with EfficientB1, EfficientB7, EfficientNetV2B0, NesNetLarge, ResNet50, VGG16, and VGG19 models demonstrates its superiority. These findings underscore the potential of the novel MRFO-based approach in achieving superior accuracy for melanoma classification. The proposed method has the potential to be a valuable tool for early detection and improved patient outcomes. 

pdf/optimizing-hyperparameters-for-improved-melanoma-12vemhng4b.pdf

Optimizing Hyperparameters for Improved Melanoma Classification using Metaheuristic Algorithm

A frequent cancer worldwide is skin cancer. Non-melanoma exists. Melanoma kills more than non-melanoma skin malignancies. Successful treatment and early diagnosis improve skin cancer survival. Cancer burden and prognosis vary depending on the diagnosis type and stage. The biopsy method used to diagnose skin cancer is imprecise. To diagnose and treat skin cancer early, onco-dermatologists must enhance diagnostic accuracy. Doctors use several tools to diagnose skin lesions. Through image processing, AI has enhanced early skin cancer diagnosis. Radiology adopted artificial intelligence (AI) sooner than dermatology. AI is now more accessible because of technology, AI-powered expert systems can detect skin cancer early. This chapter examines early skin cancer diagnosis using machine learning (ML) models and the problem of automating skin cancer diagnosis with AI algorithms. This study sheds light on past and future efforts to diagnose early skin cancer and other concerns. 

Revolutionizing Skin Cancer Diagnosis With Artificial Intelligence

Detection of melanoma with hybrid learning method by removing hair from dermoscopic images using image processing techniques and wavelet transform

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Detection of melanoma with hybrid learning method by removing hair from dermoscopic images using image processing techniques and wavelet transform