4 citations
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December 2024 in “Protein & Cell” MultiKano accurately identifies cell types in complex data better than existing methods.
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.
5 citations
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June 2023 in “Engineering Technology & Applied Science Research” This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.
This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.
April 2023 in “Journal of Investigative Dermatology” This study reports that improvements to the EczemaNet pipeline, incorporating pixel-level segmentation and data augmentation, enhanced the reliability and interpretability of assessing atopic dermatitis severity from digital images.