Classification of Visually Similar Scalp Diseases Using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
June 2026
in “
Zenodo (CERN European Organization for Nuclear Research)
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Preprint — not peer reviewed. This was posted to a preprint server or data repository. It has not been through a journal's review process, and its findings may change or not hold up.
Studysummary This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.
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