Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures

This study compares the performance of two CNN architectures, MobileNetV2 and VGG19, for classifying hair diseases using a dataset of 12,000 images. Both models were trained on images resized to 224×224 pixels and augmented through various techniques. VGG19 achieved a slightly higher accuracy of 98% compared to MobileNetV2's 97%, and it also showed better precision and recall for conditions like Alopecia Areata and Male Pattern Baldness. However, MobileNetV2 was faster and required fewer computational resources, making it suitable for resource-constrained environments. The study highlights the trade-off between accuracy and efficiency in choosing the appropriate model for automated hair disease detection.