Deep Hair-Net: A Deep Learning Approach for Diagnosing Scalp and Hair Diseases with Treatment Recommendations

Abstract

Scalp and hair diseases have a significant impact on individuals' physical, emotional, and social well-being. This research introduces 'Deep Hair-Net,' a novel system for the automated diagnosis of common scalp ailments coupled with actionable treatment recommendations. The 'Deep Hair-Net' system employs a hybrid deep learning architecture, fusing the strengths of MobileNetV2 and Inception V3 through transfer learning. This model was trained and validated on a curated dataset of 10,132 images (sourced from DermNet, ISIC, and Kaggle) covering ten distinct scalp disease classes, including dandruff, alopecia, and psoriasis. The hybrid model demonstrated high performance, achieving 95.5% classification accuracy, an F1-score of 0.92, and an AUC-ROC of 0.96. A key feature of the system is its integrated CSV-based recommendation engine, which maps the diagnostic output to specific management suggestions. This research contributes a scalable and robust AI system that closes the gap between dermatological diagnostics and practical treatment advice, offering significant value for teledermatology and resource limited environments.