5 citations
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October 2023 in “International Journal on Recent and Innovation Trends in Computing and Communication” In this study, researchers developed a novel image processing method using a multi-class support vector machine that achieved an 89.3% accuracy in classifying alopecia areata and related conditions, outperforming existing models in classification accuracy.
1 citations
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August 2023 in “arXiv (Cornell University)” This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.
The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.
15 citations
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August 2020 in “Indonesian Journal of Electrical Engineering and Computer Science” This study found that a pre-trained image processing technique accurately classified scalp conditions with 85% accuracy, suggesting potential for automated diagnosis and treatment selection.
5 citations
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July 2023 in “Journal of Autonomous Intelligence” This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.
