An Analytical and Comparative Study of Modern Intelligent Models for Skin Cancer Diagnosis - Zahraa Saad Abdul Wahid; Zahraa Ch. Oleiwi; Rasha Falah kadhem (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2026)
Early diagnosis of skin cancer especially melanoma is a significant issue because of the visual similarities of lesion types, intra class variability, and drawbacks of the manual diagnosis. Over the past years, there is a wide range of artificial intelligence (AI) methods suggested, but the results reported were quite different because of the variations in datasets, preprocessing pipelines, model architectures, and evaluation protocols. This is a systematic review article of 29 peer-reviewed studies published 2015–2025 that were identified in major scientific databases and divided into three key categories, namely traditional machine learning (ML), deep learning (DL), and transfer learning (TL) frameworks. Comparatively, the studies are assessed by aspects of characteristics of the dataset, feature representation strategies, model complexity, diagnostic performance and clinical applicability. The discussion has shown that deep and transfer learning models tend to perform better than traditional techniques of ML in terms of classification accuracy, yet various issues on the class imbalance, data heterogeneity, the ability to perform generalization as well as efficiency of computation still exist. Another critical research gap that has been found in the literature is the lack of a standardized analytical framework to compare the ML, DL, and TL models under standardized assessment procedures taking into account the real-life limitations in clinical deployment. This paper is a comparative perspective on the current intelligent skin cancer diagnostic systems in a structured way and it identifies the future research directions of stronger, scalable and clinically sound AI-based solutions.