Prediction of Alopecia Areata Using CNN

This study introduces a novel Convolutional Neural Network (CNN) architecture to improve the detection of Alopecia Areata, an autoimmune disease causing hair loss, by using an image-based dataset. Traditional diagnosis relies heavily on visual examination by doctors, which can lead to low credibility. The CNN model's performance was compared with four machine learning models: Naive Bayes, Support Vector Machine, Logistic Regression, and Decision Tree. The dataset was enhanced through image preprocessing techniques, and the CNN achieved a best accuracy of 98%, indicating its potential to streamline and enhance the reliability of Alopecia Areata detection.