YOLOv7-based automated detection platform for scalp lesions

Abstract

Objective In dermatological research, the focus on scalp and skin health has intensified, particularly regarding prevalent conditions like dandruff and erythema. This study aimed to utilize YOLOv7 model to develop an automated detection web-based system for these specific scalp lesions. Methods Utilizing a dataset of 2200 clinical images, the model's accuracy and robustness were assessed. The raw images were initially preprocessed by the Roboflow tool. We then trained and evaluated the YOLOv7 model, comparing its performance with several baseline models including YOLOv5, YOLOF, and the single-shot detector. Finally, the proposed model was integrated into a flask API-based web application using the flask-ngrok library. Results The YOLOv7 demonstrated exceptional performance, attaining a mean average precision of 98.6%, with precision and recall rates of 98.6% and 97.2%, respectively. When benchmarked against baseline models, the YOLOv7 demonstrated enhanced performance metrics both during the training phase and the testing process on unseen data. Conclusions This study not only validates the potential of YOLOv7 for scalp lesion diagnostic applications but also brings the integration of sophisticated AI models into practical healthcare solutions.