April 2026 in “International Journal of Engineering Research and Science & Technology” The study introduces an Explainable Artificial Intelligence (XAI)-based system for diagnosing hair disorders, addressing the limitations of conventional methods that often overlook the complex interplay of factors like genetics and stress. Utilizing the Flask web framework, the system employs various machine learning algorithms and introduces a novel hybrid Probabilistic Ensemble Deep Learning (PEDL) model, which combines a Probabilistic Neural Network with a Sparse Representation Classifier. This model achieves a high accuracy of 0.9950, surpassing traditional approaches. The integration of XAI techniques allows for transparent predictions, identifying key factors, assessing risks, and providing personalized recommendations for hair loss evaluation, treatment, and hormonal impact analysis.
3 citations
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July 1997 in “Current problems in dermatology” Hair restoration surgery has evolved over time, with a focus on natural-looking results and managing patient expectations, while also considering potential complications and the lifelong progression of male pattern baldness.
125 citations
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May 2007 in “Journal of The American Academy of Dermatology” The BASP classification is a detailed and accurate way to categorize hair loss in both men and women.
March 2024 in “medRxiv (Cold Spring Harbor Laboratory)” Recent selection on immune response genes was identified across seven ethnicities.
2 citations
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June 2019 in “International Journal of Dermatology” The modified hair loss classification is more detailed but less user-friendly.