A comprehensive review of deep learning and machine learning techniques for early-stage skin cancer detection: Challenges and research gaps

Abstract

Abstract Skin cancer especially when detected early can be easily treated, but its diagnosis is complicated by the minimal difference in the appearance of early lesions and the requirement of a precise diagnostic technique. The goal of this intensive literature review is to evaluate the progressive enhancements of deep learning (DL) and machine learning (ML) methods for transferring early-stage skin cancer identification in terms of accuracy and in terms of usability for real-world clinical applications. By using support vector machines, convolutional neural networks, and ensemble methods, we assess the performance of such algorithms in the classification and segmentation of skin lesions within various datasets. The challenges outlined in the review include the following: first, sparsity of data, second, variation in the looks in lesions, and third, imbalance of data within classes. Furthermore, issues that are still open to investigation are also presented, including the restricted number of algorithms for which the developed DL/ML models can be interpretable and the variability of the results assessment criteria used in different investigations. We then propose possible approaches to these issues such as data augments, multimodal learning, and the inclusion of explainable artificial intelligence approaches. The strengths of the present study consist of a comprehensive review of the limitations of contemporary methodologies and recommendations for future research on DL/ML-based systems for the early diagnosis of skin cancer. This research aims to highlight the best techniques and identify areas for future improvement. The study highlighted the key challenges of evaluating skin lesion segmentation and classification techniques, for instance, small sample size dataset, or selective and random image acquisition or even racial prejudice.

Topics & Concepts