Severity Prediction of Hair Loss in Alopecia Areata With Hybrid Deep Learning Architecture

August 2025

Deepak Banerjee, Sridhar Ramasamy, Rashi Nimesh Kumar Dhenia, Ishva Jitendrakumar Kanani, Gaurav Sharma, Manish Kumar Singla

alopecia areata hair loss deep learning severity classification automated harm detection

Studysummary In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

Our plain-language summary. Not medical advice or a treatment recommendation. Consult a qualified healthcare professional before changing treatment. Full disclaimer

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research Beyond Hair Loss: Exploring the Evolution of Androgenetic Alopecia Research Based on Text Mining and Bibliometrics

July 2024 in “Journal of Education For Sustainable Innovation”

This study analyzed word dynamics and keyword trends in androgenetic alopecia literature over a decade using natural language processing, revealing hidden patterns and linkages that could inform future research directions and policy decisions.

research Enhanced Hair Disease Classification Using Deep Learning

March 2024

In this study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.

research Ensemble of Pre-Learned Deep Learning Model and an Optimized LSTM for Alopecia Areata Classification

7 citations , 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.

research Prediction of Alopecia Areata Using CNN

15 citations , May 2023

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

research FRCNN Based Deep Learning for Identification and Classification of Alopecia Areata

9 citations , February 2023

This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

research Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder

July 2022 in “International Journal of Applied Pharmaceutics”

This research explored the use of machine learning and deep learning methods to accurately identify alopecia areata in humans by analyzing facial images and demonstrated the potential of these techniques for medical, security, and commercial applications.

research Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach

8 citations , August 2021 in “Computational and Mathematical Methods in Medicine”

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.