Recognizing common skin diseases in the Philippines using YOLOv5 deep learning

This study addresses the pressing need for precise skin disease diagnostics in the Philippines. Leveraging YOLOv5 deep learning, the research verifies an accurate categorization system for prevalent conditions like Acne, Leprosy, and Psoriasis. Comparative analysis with ANN and SVM classifiers demonstrates the transformative impact of this approach on skin disease diagnosis. By expediting care and supporting dermatologists, the study charts a course for healthcare advancements, bridging critical gaps in Filipino dermatology. The methodology outlines the implementation of a Skin Disease Detection Model utilizing YOLOv5 Deep Learning within a robust Medical Image Analysis framework. Rigorous evaluation metrics highlight YOLOv5L's accuracy in identifying Leprosy and Warts, despite challenges in recognizing diseases like Atopic Dermatitis and Keratosis Pilaris. The research underscores YOLOv5L's potential while emphasizing the need for dataset refinement and model optimization. Recommendations include diversifying datasets, exploring hyperparameters, and incorporating additional disease categories, offering valuable insights for advancing dermatological diagnostics in the Philippines.