The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
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January 2023 in “IEEE access” Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
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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.
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March 2024 in “Skin research and technology” A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
October 2023 in “Sinkron” The system can accurately classify hair diseases with 94.5% accuracy using a CNN.