Identification of Hair and Scalp Diseases by Machine Learning Based Analysis

Diagnosing hair and scalp diseases has traditionally required lengthy manual examination by dermatologists. Individual machine learning models have shown promise but often fall short in handling the complex variability of dermatological conditions. This project proposes an ensemble model that combines Convolutional Neural Networks (Convolutional NN) and Random Forest (RF) algorithms to improve accuracy and reliability. The Convolutional NN extracts intricate image features from scalp and hair data, while the RF classifier refines these insights, minimizing overfitting and enhancing classification of non-linear patterns. This combined approach leverages Convolutional NN’s feature extraction capabilities and RF's robust classification to deliver more balanced, generalized predictions. Trained on a diverse dataset of conditions like alopecia, psoriasis, and healthy hair, the model achieves higher diagnostic accuracy than single-model solutions. A user-friendly interface also provides an accessible tool for patients and professionals, supporting early detection, proactive care, and better treatment outcomes