Leveraging Deep Neural Networks to Uncover Unprecedented Levels of Precision in the Diagnosis of Hair and Scalp Disorders

The study introduces a deep neural network model based on the Xception architecture for diagnosing hair and scalp disorders, achieving a high accuracy rate of 92%. The model outperforms existing models like VGG19, Inception, and DenseNet, with precision, recall, and F1-scores for individual disease classes consistently exceeding 80%. The model's AUC score of 0.99 indicates robust discriminatory power. Key enhancements include ReLU activation, dense layers, and GAP. Explainable AI techniques like Grad-CAM and Saliency Map provide transparency in decision-making, fostering clinical trust. The model's high performance metrics suggest significant potential for improving patient care by reducing misdiagnoses and facilitating early detection. Future research should focus on expanding the dataset to enhance the model's robustness and generalizability.