Hair Loss Stage Prediction Using Deep Learning

Hair losses diseases are common and pose challenges in diagnosing accurately and promptly. Traditional diagnostic methods involve visual and medical tests by dermatologists, leading to delays that worsen conditions. To address this, a deep learning solution using a 2D convolutional neural network (CNN) was implemented, effectively predicting hair loss categories: alopecia, psoriasis, and folliculitis. Challenges included limited dataset access and diverse online images affecting model precision. Besides the model's success, a dataset was created using factors like SSB, Ageing, hierarchical characteristics, and Stress, valuable for future research. This study's significance lies in aiding timely hair loss identification via neural network applications, benefiting both medical professionals and individuals. The project's approach hinges on leveraging the power of deep learning models to discern intricate patterns within frontal facial images. The utilization of Convolutional Neural Networks (CNNs) will enable the automatic extraction and learning of relevant information from the images, hence enabling the capture of minute variations in hair density, coverage, and distribution. These learned features will subsequently serve as the foundation for an accurate and comprehensive classification system that aligns with the Hamilton-Norwood scale's progressive stages of hair loss. To sum up, the goal of this research study is to show that deep learning techniques can automatically identify different stages of hair loss from frontal facial photos. Through the integration of neural networks and image processing skills, this research aims to further the development of hair loss diagnosis and treatment approaches, ultimately improving the lives of those who suffer from this illness.