Hair Loss Stage Prediction Using different CNN models

Hair loss disorders are prevalent and present challenges in prompt and accurate diagnosis. Traditional diagnostic approaches, involving visual inspections and medical tests by dermatologists, often result in delays exacerbating the conditions. In response, a deep learning solution utilizing a 2D convolutional neural network (CNN) was deployed to effectively predict various hair loss categories such as alopecia, psoriasis, and folliculitis. However, this endeavor encountered obstacles including limited access to datasets and the presence of diverse online images impacting the model's precision. In addition to the success of the model, a dataset was curated incorporating factors like SSB, aging, hierarchical characteristics, and stress, which holds promise for future investigations. The significance of this study lies in its contribution to timely identification of hair loss through neural network applications, benefiting both medical practitioners and affected individuals. The methodology of the project revolves around harnessing the capabilities of deep learning models to discern subtle patterns within frontal facial images. Utilizing Convolutional Neural Networks (CNNs) facilitates automatic extraction and comprehension of relevant information from images, enabling the detection of minute variations in hair density, coverage, and distribution. These learned features serve as the groundwork for an accurate classification system aligning with the progressive stages of hair loss as defined by the Hamilton-Norwood scale. In summary, this research endeavors to demonstrate the efficacy of deep learning techniques in automatically identifying different stages of hair loss from frontal facial photos. By integrating neural networks and image processing expertise, the aim is to advance hair loss diagnosis and treatment methodologies, ultimately enhancing the quality of life for individuals affected by this condition