Paper Contents
Abstract
In the realm of medical health, accurate disease diagnosis is paramount, with dermatology posing particular challenges. Dermatologists often rely on extensive testing, patient history reviews, and data analysis for precise diagnoses. Consequently, there is a need for a swift and reliable method to ensure accurate diagnoses. While various machine learning approaches have been explored for dermatological diagnosis, many lack high accuracy. This study introduces a MATLAB-based system designed to swiftly identify and classify skin lesions as normal or benign. Employing the K-nearest neighbor (KNN) method for classification, the system achieves a remarkable accuracy of 98% in distinguishing between normal and pathological skin lesions.
Copyright
Copyright © 2024 M Nancy. This is an open access article distributed under the Creative Commons Attribution License.
