Hair Loss Stage Classifcation using CNN and Transfer Learning Approaches

In the field of dermatology, identifying the exact stage of hair loss is essential for early diagnosis, customized treatment planning, and improved patient results. In this study, the performance of three deep learning methods ResNet, VGG16, and a custom convolutional neural network (CNN) for categorizing scalp photos into seven distinct stages of hair loss is compared. A balanced data set of 1,540 images was preprocessed to create subsets for training, validation, and testing. Based on experimental results, the custom CNN achieved a high accuracy of 97.40%, precision of 0.96, recall of 0.98, and F1 score of 0.97, demonstrating a great balance between precision and generalization. With the maximum accuracy of 99.35%, ResNet showed overfitting, which resulted in a lower recall of 0.89. The domain-specific character of the data set made it difficult for VGG16 to generalize, despite its consistent performance with 96.74% precision. The results show that lightweight bespoke architectures may be more appropriate for particular datasets, providing steady and consistent predictions, even when transfer learning methods can produce high accuracy. In addition to highlighting the potential of enhanced CNN models for practical applications in clinical and telemedicine settings, this study adds to the expanding corpus of research on AI-driven dermatological diagnoses.