An In-Depth Review of Leveraging Deep Learning Advancements for Enhanced Skin Cancer Detection and Classification

Ahwaz Darweesh Hayder; Jwan  Najeeb Saeed

doi:10.65542/djei.v2i1.26

Authors

Ahwaz Darweesh Hayder Information Technology Management Department, Duhok Polytechnic University
Jwan Najeeb Saeed Artificial Intelligence Department, Duhok Polytechnic University https://orcid.org/0000-0001-7829-3139

DOI:

https://doi.org/10.65542/djei.v2i1.26

Keywords:

Deep learning; skin lesions classification; convolutional neural networks; transfer learning; explainable AI.

Abstract

Skin cancer is a prevalent form of cancer caused by the abnormal growth of skin cells, most commonly resulting from exposure to ultraviolet radiation. Early detection is essential for improving clinical outcomes, highlighting the need for advanced and reliable diagnostic approaches. Traditional diagnostic methods often face significant challenges due to high variability arising from the subjective nature of assessments and their reliance on specialists, whose performance is constrained by conventional techniques such as dermoscopy and histopathology. Recent advancements in deep learning have significantly transformed the field of dermatology by enabling automated and reliable skin lesion classification. This study presents an in-depth review of state-of-the-art deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transfer learning, and generative adversarial networks (GANs). In addition, the study examines the application of vision transformers (ViTs), which have demonstrated strong capabilities in capturing comprehensive contextual information in skin lesion analysis. Furthermore, this review explores the integration of explainable artificial intelligence (XAI) as well as hybrid and collaborative strategies to enhance model interpretability and reliability. Therefore, this review aims to establish a solid foundation for future research in the automated classification of skin cancer.

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