ARTIFICIAL INTELLIGENCE BASED EARLY DETECTION OF SKIN CANCER USING DERMOSCOPIC IMAGE ANALYSIS

Abstract

Skin cancer, particularly malignant melanoma, poses a significant global health burden, with early detection being the most critical factor for improving patient survival rates. Dermoscopy has enhanced diagnostic accuracy over naked-eye examination by revealing subsurface structures, but interpretation remains subjective and dependent on clinician expertise. Artificial intelligence (AI), especially deep learning architectures such as convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models, has demonstrated performance comparable to or surpassing board-certified dermatologists in classifying dermoscopic images of pigmented lesions. This review comprehensively examines AI-driven approaches for early skin cancer detection, covering preprocessing techniques (hair removal via DullRazor, E-Shaver, and inpainting), segmentation (U-Net variants, ResUNet++, TransUNet), classification (transfer learning with EfficientNet, ResNet, DenseNet, Xception, and ViT-based models), and multimodal fusion incorporating clinical metadata (age, sex, lesion location, history). Performance metrics on benchmark datasets (ISIC, HAM10000, DDI) show accuracies ranging from 86% to 98.5%, with hybrid and ensemble models often achieving superior results. Explainable AI (XAI) techniques Grad-CAM, Eigen-CAM, LIME address the “black-box” issue by providing visual justifications aligned with dermatological criteria (pigment networks, blue-white veils). Challenges include dataset bias toward light skin tones, class imbalance, generalization across populations, and regulatory hurdles (EU AI Act, FDA guidelines). Future directions emphasize multimodal integration (dermoscopy + reflectance confocal microscopy + optical coherence tomography), federated learning for privacy-preserving training on diverse datasets, and deployment in teledermatology and primary care settings to democratize expert-level screening. AI-assisted dermoscopic analysis holds transformative potential for reducing diagnostic delays, minimizing unnecessary biopsies, and improving outcomes in skin cancer management.