Quantitative Analysis and Development of Alopecia Areata Classification Frameworks

    January 2024 in “ Journal of Emerging Investigators ”
    Ayushmaan Dubey, A. Morales
    Studysummary In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.
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    Research cited in this study 7

    1. Quantitative Analysis and Development of Alopecia Areata Classification Frameworks Journal of Emerging Investigators · 2024
    2. AB-MTE Deep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata Engineering Technology & Applied Science Research · 2023
    3. Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition Indian journal of science and technology · 2023
    4. Hair and Scalp Disease Detection Using Machine Learning and Image Processing European Journal of Information Technologies and Computer Science · 2023
    5. Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN Journal of Imaging · 2022
    6. An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach Journal of Pharmaceutical Negative Results · 2022
    7. ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health IEEE Access · 2020

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