April 2023 in “Journal of Investigative Dermatology” The AI model somewhat predicts lymph node status in melanoma patients using skin sample images.
112 citations
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November 2023 in “Nano-Micro Letters” Nanozymes show promise for effective and safe cancer treatment.
1 citations
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May 2025 in “Journal of Digital Information Management” The study evaluates the effectiveness of various convolutional neural network (CNN) architectures, including VGG16, VGG19, Inception-V3, ResNet50, and ResNet152, for classifying scalp and hair diseases using a dataset of 12,530 images. VGG16 and VGG19 outperform other models in accuracy across all disease categories, with test accuracies of 96.81% and 96.73%, respectively, demonstrating their robustness for this application. Inception-V3 also performs well, particularly in complex cases like psoriasis, with a test accuracy of 95.13%. ResNet models underperform due to their complexity and the limited dataset size. The study suggests that expanding datasets and exploring ensemble models could further enhance diagnostic accuracy and reliability, aiming to improve automated diagnostic methods and clinical decision-making.
4 citations
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May 2024 in “INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT” AI can accurately diagnose hair and scalp conditions and suggest treatments.
1 citations
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January 2023 in “IEEE access” Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.