SkinCancer Identification: Advancing Early Diagnosis with Convolutional Neural Networks

Abstract: Skin cancer is a major global health burden, and early detection markedly improves outcomes. Yet many patients face delayed diagnosis because specialist dermatology expertise is scarce or unevenly distributed, especially in underserved regions. We propose an AI-driven decision-support system that analyzes clinical and dermoscopic images to flag suspicious lesions for clinician review. Trained on large, curated image datasets, the model learns visual patterns linked to malignancy, analogous to experiential learning in clinical practice. In reader studies, deep learning systems have achieved dermatologist-level performance and, when used alongside clinicians, can enhance diagnostic accuracy and triage efficiency. Integrated responsibly into workflows, such tools may expand screening reach, shorten time to specialist assessment, and enable earlier intervention while complementing—not replacing—clinical judgment.

Keywords: Artificial Intelligence, Skin Cancer, Image Analysis, Deep Learning, Dermatoscopy, Diagnostic Tool