Advancing Hair Disease Diagnostics: A Deep Learning Approach Using Inception-ResNet V2 for Multi-Class Classification

December 2024

Pratham Kaushik, Sunila Choudhary

Alopecia Areata Male Pattern Baldness Folliculitis Seborrheic Dermatitis Head Lice Alopecia Baldness

Studysummary This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.

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research Leveraging Deep Neural Networks to Uncover Unprecedented Levels of Precision in the Diagnosis of Hair and Scalp Disorders

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In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

research A Review of Genotrichoses and Hair Pathology Associated with Inherited Skin Diseases

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This review highlights the significant role of next-generation sequencing in uncovering the genetic basis of hair disorders, identifying 28 nonsyndromic conditions linked to specific genes.

research Female Pattern Hair Loss: A Comprehensive Review

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research ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health

74 citations , January 2020 in “IEEE Access”

This study reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.