7 citations
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October 2023 in “Journal of Intelligent & Fuzzy Systems” This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.
50 citations
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December 2011 in “Skin Research and Technology” In this study, the researchers reported that their novel algorithm for hair restoration in dermoscopy images achieved high accuracy and texture preservation, outperforming other techniques in diagnostic accuracy and texture quality measures.
1 citations
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January 2024 in “Wiadomości Lekarskie” This study evaluated a new computer-aided detection system for identifying Breast Arterial Calcification in mammograms, achieving 70% accuracy, but highlighted the need for a larger dataset to explore its relationship with cardiovascular diseases.
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.
January 2024 in “Wiadomości Lekarskie” This study developed an AI-driven method for classifying cells in Follicular Lymphoma cases, achieving a 63% F1-score, precision, and recall in distinguishing centroblasts from other cell types using whole slide images at x20 resolution.