Skin Disease Detection Using RestNet-18

Skin diseases represent one of the most prevalent health conditions worldwide, affecting millions of individuals and requiring timely, accurate diagnosis for effective treatment outcomes. Traditional dermatological diagnosis methods rely heavily on clinical expertise and visual examination, resulting in accessibility limitations and potential diagnostic inconsistencies, particularly in underserved regions with limited specialist availability. This comprehensive study presents the development and implementation of an AI-driven skin disease detection system leveraging deep learning technologies for automated classification and diagnosis support. Through systematic implementation of Convolutional Neural Networks (CNNs), specifically the ResNet-18 architecture with transfer learning techniques, this research demonstrates remarkable accuracy in classifying seven distinct categories of skin diseases including Melanoma, Basal Cell Carcinoma, and other critical dermatological conditions. The proposed system integrates advanced computer vision preprocessing, real-time web-based deployment, and intelligent conversational support through Google Dialogflow integration. Experimental validation using the ISIC 2018 dataset achieved classification accuracy of 87%, with the system demonstrating robust performance across diverse imaging conditions and patient demographics. The findings indicate significant potential for AI-driven systems to enhance dermatological screening accessibility while maintaining clinical-grade diagnostic reliability and supporting early detection initiatives in resource-constrained environments.