A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification

March 2026

V. Jaganraja, Daiva Dinesh Kumar Chowdary, Mahendra Vakkalagadda, S. Vinoth Kumar, Sankar Ganesh Karuppasamy, Rayappan Lotus

scalp diseases hair fall VGG16 ResNet50 DenseNet121 EfficientNet-B0 U-Net MobileNetV3 Intersection over Union IoU CNN convolutional neural networks deep learning machine learning neural networks

Studysummary This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

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

The document discusses a hybrid deep learning system designed for the automatic detection of scalp diseases and classification of hair fall stages. It highlights the prevalence of scalp disorders such as Alopecia Areata, Psoriasis, Folliculitis, and Seborrheic Dermatitis, which cause visible scalp inflammation, hair loss, and psychological effects, significantly impacting quality of life. Early detection is crucial to prevent irreversible damage and long-term hair loss. Traditional diagnostic methods rely on subjective visual evaluations by dermatologists, which can be time-consuming and inconsistent. The advancement in deep learning technology offers a promising solution for objective and rapid assessment of these conditions through automated image-based analysis.

View this study on ieeexplore.ieee.org →

Discuss this study in the Community →

Research cited in this study

2 / 2 results

research Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures

1 citations , November 2024

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

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.