5 citations
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January 2025 in “BMC Medical Informatics and Decision Making” This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.
January 2021 in “Lecture notes in networks and systems” In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.
1 citations
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January 2023 in “IEEE access” This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.
June 2026 in “Zenodo (CERN European Organization for Nuclear Research)” In this study, researchers developed ScalpViT, a novel deep learning model, to improve the automated diagnosis of visually similar scalp diseases, achieving 94.3% accuracy and outperforming existing models like ResNet-50 and EfficientNet-B3 when tested on a diverse dataset of 7,000 images.
June 2026 in “Zenodo (CERN European Organization for Nuclear Research)” 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.
