A Novel Approach to Vitiligo Diagnosis using Artificial Neural Networks and Dermatological Image Analysis

Abstract

This study presents a novel approach to diagnosing vitiligo through the use of artificial neural networks (ANNs) and dermatological image analysis. Leveraging advanced image processing techniques, we analyzed skin lesion images to identify vitiligo with greater precision and speed. Our approach utilizes a pre-trained convolutional neural network (CNN) model, fine-tuned on a dataset of dermatological images to extract critical features from the lesions. The ANN then processes these features to classify the presence or absence of vitiligo. By incorporating patient demographic data along with image analysis, we improved the diagnostic accuracy of the model. This method demonstrates significant potential in reducing diagnostic error and aiding dermatologists in clinical decision-making. The results show improved prediction performance and offer a more efficient, non-invasive alternative for diagnosing vitiligo, with implications for future clinical applications and automated dermatological analysis.