A Machine Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health

January 2024

P. Bharath Kumar Chowdary, Ameti Sadhana, Chintamaneni Mahalakshmi, Kamala Priya Vege, K. Hemant Kumar Reddy, Srija Tulasi

alopecia areata contact dermatitis folliculitis head lice lichen planus male-pattern baldness psoriasis seborrheic dermatitis telogen effluvium tinea capitis VGG-19 hair loss scalp health

Studysummary This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

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

The project aims to develop an automatic and highly accurate machine learning-based recognition technique for the classification of healthy hair and damaged scalp. The system uses a machine learning algorithm to identify and classify various hair diseases, including alopecia areata, contact dermatitis, folliculitis, head lice, lichen planus, male-pattern baldness, psoriasis, seborrheic dermatitis, telogen effluvium, and tinea capitis. The VGG-19 model is used in this study to train and test the prediction of classifying each condition as one of the hair diseases. This system could potentially help in diagnosing hair loss, which results from at least 30% of scalp and hair issues caused by poor daily habits, an unbalanced diet, high stress levels, and environmental toxins.

View this study on link.springer.com →

Discuss this study in the Community →

Research cited in this study

3 / 3 results

research Genetic Prediction of Male Pattern Baldness Based on Large Independent Datasets

3 citations , November 2022 in “European Journal of Human Genetics”

This study developed new genetic prediction models for male pattern baldness with improved accuracy by utilizing a large set of markers and independent datasets, making them the most reliable available for this trait.

research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

8 citations , January 2022 in “Sensors”

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

research Genetic Prediction of Male Pattern Baldness

133 citations , February 2017 in “PLoS Genetics”

In this study, researchers used genetic data from over 52,000 men to identify over 250 genetic loci associated with severe hair loss and developed a predictive algorithm for determining hair loss risk.