Automated Hair Disease Classification Using VGG19: A Deep Learning Approach for Enhanced Diagnostic Accuracy

The research concerns the classification of hair diseases by VGG19 CNN model training. In the paper, several types of hair diseases were used for training and testing of the model with the hope of improving diagnostic support for dermatologists. Because the incidence of hair and scalp disorders is increasing today, automatic detection systems play an important role in early diagnosis and treatment rather than the time-consuming way of manual analysis of medical images. VGG19 architecture was chosen because its deep layers extract fine features. After optimization and fine-tuning, the model achieved a test accuracy of 98.67% and a test loss of 5.34%, so the model has been very effective in identifying hair diseases from dermatoscopic images. Inter-model assessment did exist on the basis of a confusion matrix, which depicted that only negligible cases of misclassifications occurred among the classes. The testing phase took 11.42 seconds to complete because of the model’s efficiency and its suitability for real-world clinical applications. Ultimately, VGG19 seems to be a promising gadget for hair disease diagnosis without human intervention, having immense potential to raise the accuracy and speed of diagnosis in dermatological practice. Further integrative studies are recommended with the clinical setting to test wider perspectives and validation.