Pre-trained classification of scalp conditions using image processing

The study focused on developing a pre-trained classification system for scalp conditions using image processing techniques. It aimed to provide a more affordable and convenient method for monitoring scalp conditions compared to traditional methods. The process involved pre-processing scalp images, extracting features such as color, texture, and shape, and using a support vector machine (SVM) to classify conditions into alopecia areata, dandruff, and normal. The system was tested on 120 images and achieved an accuracy of 85%. The study suggested that this method could help in automatically classifying scalp conditions and assist users in selecting appropriate treatments.