Deep Learning Powered Skin Disease Detection with Integrated Dermatologist Locator

Skin diseases such as acne, rosacea, psoriasis, and eczema are common dermal conditions that can have a significant impact on a person’s wellbeing. Prompt and proper identification is critical for effective therapy, but traditional diagnosing techniques through traditional evaluations performed by a dermatologist, taking a significant period and contributing towards delayed therapy. To counter such an issue, an automated system for prediction of dermal disease through deep learning is proposed in this work. EfficientNet, a high-performance and efficient Convolutional Neural Network (CNN) model, is utilized in this work for its high performance and efficiency in medical image classification. EfficientNet is trained with a high-resolution dermal image collection, and proper classification of a variety of dermal ailments can be facilitated through it. Users can upload an image, and/or a verbal statement, and then, through processing, the system can make a prediction about a disease, and a feature for a locator for nearby derms is included for guiding a patient towards a nearby doctor for expert therapy. In comparative analysis, our proposed model performed with an accuracy of 96.7%, and its efficiency in dermal diagnostics can have a significant impact in terms of early therapy, optimized patient therapy, and contribution towards improvement in worldwide therapy.