Deep Learning-Based Skin Disease Detection and Classification: A Systematic Literature Review

Zhehat Abdulqader; Araz Abrahim

doi:10.65542/djei.v2i1.19

Authors

Zhehat Abdulqader College of Education, University of Zakho https://orcid.org/0009-0001-5638-9065
Araz Abrahim Technical Institute of Duhok, Duhok Polytechnic University https://orcid.org/0009-0005-7671-0724

DOI:

https://doi.org/10.65542/djei.v2i1.19

Keywords:

Deep Learning, Convolutional Neural Networks (CNN), Transfer Learning, Image Processing, Skin Lesions, Image Classification, Dermatoscopic Image

Abstract

Recent advances in deep learning have significantly transformed medical diagnostics, particularly in dermatology. Accurate skin disease detection and classification areessential for effective treatment and improved patient outcomes. This systematic review examines deep learning approaches, including Convolutional Neural Networks (CNNs) and transfer learning, for automated dermatological diagnosis. Public datasets such as HAM10000 and ISIC play a key role in training robust models; however, challenges including dataset imbalance, disease heterogeneity, and overfitting remain. Techniques such as ensemble learning, attention mechanisms, explainable artificial intelligence, data augmentation, hybrid models, and task-specific loss functions have been shown to enhance accuracy, robustness, and interpretability. This study follows a systematic review methodology in accordance with the PRISMA guidelines. The review synthesizes 17 studies published between 2021 and 2024, highlighting the potential of deep learning to support scalable and reliable dermatological diagnostic systems.

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