research Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
In this study, researchers developed a hybrid deep learning model called ScalpViT that accurately diagnosed scalp diseases with 94.3% accuracy, surpassing existing methods like ResNet-50 and EfficientNet-B3, and providing visual explainability for clinicians using GradCAM and Attention Rollout techniques.





