AUTOMATED SKIN LESION ASSESSMENT USING SKINVISION AI WITH DEEP LEARNING TECHNIQUES

Abstract

Skin cancer is one of the most prevalent forms of cancer worldwide, and its early detection is critical for effective treatment and improved patient outcomes. Traditional diagnostic approaches rely heavily on dermatological expertise, which may not always be accessible in resource-limited areas. Recent advances in artificial intelligence (AI) and deep learning have enabled automated systems to assist dermatologists in detecting and classifying skin lesions with high accuracy. This paper presents an automated skin lesion assessment framework using SkinVision AI enhanced with deep learning models, specifically Convolutional Neural Networks (CNNs) and transfer learning architectures. The system analyzes dermoscopic images, extracts discriminative features, and classifies lesions into malignant or benign categories with robust precision. Compared to conventional diagnostic methods, the proposed solution achieves superior performance in terms of sensitivity, specificity, and accuracy. Experimental evaluations on benchmark dermatological datasets validate the system’s effectiveness, achieving up to 94% classification accuracy. The study highlights the potential of AI-driven dermatological tools in assisting medical professionals, enabling early diagnosis, and democratizing access to skin cancer screening.