Automated Classification of Skin Lesions using Convolutional Neural Networks

Sharad Mathur; Ashish Rai

Automated Classification of Skin Lesions using Convolutional Neural Networks

Author(s)	Dr. Sharad Mathur, Dr. Ashish Rai
Country	India
Abstract	The incidence of skin cancer is a growing global health concern, making early and accurate detection essential for patient survival. While traditional diagnosis relies heavily on the visual inspection of dermoscopic images by expert dermatologists, this process is highly subjective and time-consuming. Convolutional Neural Networks (CNNs) have emerged as powerful tools for automating this diagnostic workflow. This paper presents a comprehensive review and architectural analysis of automated skin lesion classification using CNNs. We detail the end-to-end pipeline, including advanced preprocessing techniques like the DullRazor algorithm for artifact removal, and evaluate the performance of state-of-the-art networks such as ResNet, EfficientNet, and hybrid Vision Transformers on benchmark datasets like HAM10000. Furthermore, we address the critical barriers to clinical deployment: the "black-box" nature of neural networks, mitigated through Explainable AI (XAI) frameworks like Grad-CAM and SHAP, and the persistent demographic biases tied to skin tone representation. By synthesizing these elements, this paper provides a roadmap for developing transparent, equitable, and highly accurate dermatological clinical decision support systems.
Keywords	Convolutional Neural Networks, Skin Lesion Classification, Dermoscopy, Explainable AI, Algorithmic Fairness, Melanoma
Field	Computer Applications
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-29

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Automated Classification of Skin Lesions using Convolutional Neural Networks

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