Optimized VGG19 Architecture for Precise and Efficient Multi-Class Hair Disease Classification

The study presents an optimized VGG19 deep learning model for classifying hair diseases, achieving a high classification accuracy of 98.64% using a balanced dataset of 12,000 images across ten hair disease classes. The model effectively distinguishes conditions such as Alopecia Areata, Folliculitis, and Male Pattern Baldness, thanks to data pre-processing techniques and strategic model training enhancements. This advancement offers a reliable tool for hair disease diagnosis, with potential integration into mobile diagnostics for clinical and remote applications. Future improvements could involve incorporating diverse datasets and advanced architectures to enhance model robustness.