Deep Learning based System for Skin Disease Detection

Skin diseases are among the most prevalent health concerns worldwide, with millions affected annually by conditions ranging from common infections to serious illnesses such as melanoma. These disorders can significantly impair quality of life and, in some cases, lead to severe medical complications if not diagnosed and treated promptly. However, access to dermatological care remains limited in many regions, particularly in rural and underserved areas, where delays in diagnosis are common due to a shortage of trained professionals and diagnostic infrastructure. In this context, artificial intelligence (AI) presents a promising avenue for enhancing dermatological diagnostics. This research introduces a deep learning-based system for the automatic classification of skin diseases using image data. The proposed model is built upon ResNet-50, a state-of-the-art convolutional neural network (CNN) architecture known for its ability to extract and learn complex hierarchical features from visual inputs. A diverse and curated dataset of labeled skin disease images was used to train and evaluate the model. The system effectively identifies and classifies conditions such as eczema, psoriasis, acne, and melanoma, each of which presents with visually distinct characteristics. This research not only underscores the viability of deep learning in medical image classification but also highlights the broader impact such technologies can have in democratizing healthcare.