July 2026 in “Journal of King Saud University - Computer and Information Sciences” This study introduced a novel framework that significantly improves the accuracy of alopecia areata lesion segmentation in semi-supervised scenarios, outperforming existing methods and aiding in the disease's diagnosis, treatment, and staging, which can impact quality of life and mental well-being.
May 2023 in “Indian journal of science and technology” This study found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.
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.
December 2021 in “Acta dermato-venereologica” This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.
2 citations
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September 2025 in “JDDG Journal der Deutschen Dermatologischen Gesellschaft” This study found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.
