Dermatology 2.0: Deploying YOLOv11 for Accurate and Accessible Skin Disease Detection: A Web-Based Approach

Skin disorders are common and require diagnosis and treatment in a timely manner. In traditional diagnostics, great demands are made on the time and interpretation of the results. To cope with this, we introduce YOLOv11, an enhanced deep learning model designed for skin disease detection and classification. The model integrates EfficientNetB0 as the backbone for feature extraction and ResNet50 in the head for robust classification and localization. Our model was trained on a dataset of 10 common skin diseases to ensure robustness and accuracy; we were able to classify the diseases with a mean Average Precision (mAP) of 89.8%, a precision of 90%, and a recall of 88% on the test dataset. This model was developed in the form of a web application based on Streamlit, which was used for easy uploading of pictures by both clinicians and patients for threshold diagnostics. This upsurge in technology allows for treatment without visitation, making skin disease diagnosis more dynamic.