Hair & Scalp Disease Detection Using Machine Learning & Image Processing

Hair & Scalp Disease Detection Using Machine Learning & Image Processing

Ankit Wakpaijan1, Astha Tupkari2, Ashwini Dhote3, Raj Shriwastav4, Sanghsheel Umare5 ,Prof.D.A.Kapgate6

Department of Electronics & Communication Engineering

Priyadarshini College of Engineering

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Abstract - This study predicts hair disorders and provides tailored therapeutic suggestions using a deep learning model based on the VGG (Visual Geometry Group) architecture. A common dermatological problem, hair diseases can have a big impact on a person's physical and mental well-being. Early and accurate diagnosis as well as tailored treatment recommendations are essential for effective management. Inspired by the VGG design, we built a convolutional neural network (CNN) model in this study to analyze images of hair and scalp conditions. The model was trained using a vast and diverse set of images depicting various hair and scalp conditions. Transfer learning was used to modify the pre-trained VGG model so that it could recognize particular characteristics associated with particular hair issues. The prediction model consistently and reliably recognized a variety of hair problems that included dandruff, fungal infections, and alopecia. Reduction of false-positive and false negative outcomes in diagnosing diseases is dependent on high sensitivity and specificity. The suggested AI-based system has the potential to transform the dermatology field by providing prompt and accurate diagnosis of hair diseases and customized treatment recommendations. This study contributes to the ongoing efforts to use artificial intelligence and deep learning to improve healthcare outcomes, especially in the dermatology and skincare domains.

Key Words:  Convolutional Neural Network (CNN), Deep Learning, Hair Disorders, Visual Geometry Group (VGG).