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

Many people experience scalp and hair issues like dandruff, folliculitis, hair loss, and oily hair as a result of poor daily habits, an unbalanced diet, elevated levels of stress, and environmental toxins. Hair loss results from at least 30% of these issues. The objective of this project is to develop an automatic and extremely accurate ML-based recognition technique as well as an accurate classification of healthy hair and damaged scalp using a machine learning algorithm. Alopecia areata, contact dermatitis, folliculitis, head lice, lichen planus, male-pattern baldness, psoriasis, seborrheic dermatitis, telogen effluvium, and tinea capitis are among the hair diseases that the VGG-19 model in this study will be used to train and test to predict the results of classifying each condition as one of the hair diseases.