Paper Contents
Abstract
Early and accurate identification of skin diseases is essential for effective treatment, yet manual diagnosis can be challenging due to the visual similarity between many dermato- logical conditions and the shortage of specialized clinicians. This study presents a machine learningbased approach for automated skin disease detection using dermoscopic and clinical images. The system incorporates image preprocessing, feature extraction, and a classification model trained to distinguish among multiple skin conditions. Convolutional Neural Networks (CNNs) are employed to automatically learn discriminative patterns related to texture, color, and lesion shape. The proposed model is evaluated on a publicly available dataset and demonstrates strong classification performance, highlighting its potential as a supportive diagnostic tool. By providing rapid and consistent predictions, the system aims to assist healthcare professionals in screening skin diseases and improving access to dermatological care.
Copyright
Copyright © 2025 Vaishnavi K N, Darshan B E, Saniya D, Yashaswini B P. This is an open access article distributed under the Creative Commons Attribution License.
