Skin Disease Detection Using Image Processing and CNN

Abstract: The rising prevalence of skin problems, combined with a dearth of dermatologists—particularly in rural and underserved areas—has generated a pressing demand for novel technologies that aid in early detection and diagnosis. To address this, we introduce an AI-powered solution that helps patients, general practitioners, and dermatologists by providing an efficient and user-friendly preliminary screening tool. Our method uses Convolutional Neural Networks (CNNs), specifically the Mobile Net architecture, to evaluate high-resolution dermoscopic and clinical skin images and accurately classify various dermatological diseases. The algorithm, which was trained on a large, labelled dataset, can recognize a variety of skin illnesses, including melanoma, eczema, psoriasis, and acne.Mobile Net’s lightweight architecture makes it ideal for deployment on mobile and edge devices, allowing for use in distant environments and medical applications. This system is designed to serve as a decision-support tool, enhancing diagnosis accuracy, minimizing delays, and streamlining healthcare delivery without displacing medical experts. With additional improvement and clinical validation, the system has the potential to have a global impact by improving dermatological treatment accessibility and efficiency.