Citation :

Mayuri Patil, Suraj Redekar, "Deep Learning for Melanoma Detection: A Concise Review of Datasets, Architectures, and Clinical Challenges," International Journal of Computer Science and Engineering, vol. 13, no. 5, pp. 1-8, 2026. Crossref, https://doi.org/10.14445/23488387/IJCSE-V13I5P101

Abstract

Melanoma continues to be one of the most prevalent causes of mortality cases involving skin cancers. Therefore, early diagnosis is vital to help increase patient survival rates. This review article aims to provide in-depth information on deep learning approaches applied to melanoma detection and analyze papers on this subject written between 2016 and 2025. Also, the paper will highlight publicly available databases, novel architectures, and existing difficulties associated with their application. In particular, it will explore the features of three popular datasets (ISIC, HAM10000, and PH2) and analyze their strengths and weaknesses. The focus will be on such factors as the class imbalance problem and the underrepresentation of various demographics. In addition, different architectures used for melanoma detection will be compared, namely VGG, ResNet, Inception, and novel Vision Transformer networks based on their efficiency and capacity to classify melanoma. Finally, major challenges faced during implementation, including overfitting of models, difficulty in their generalization to other populations, and the problem of black box methods, will be considered. At the same time, potential future directions of research, such as the application of explainable artificial intelligence, federated learning techniques, and the use of various demographic groups as training data, will be presented.

Keywords

Melanoma, Deep Learning, Explainable AI, Clinical Data, Skin Cancer.

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