An Advance Technology for Ulcer Detection Using Machine Learning Methods

Abstract

Ulcers represent a significant global health burden, necessitating early, accurate, and automated diagnostic tools. This paper proposes a methodology for developing an "Ulcer Dictionary"—a comprehensive, automated diagnostic system—using Machine Learning (ML) and Deep Learning (DL) techniques on publicly available image datasets. The dictionary focuses on the automated detection and classification of two clinically significant ulcer types: Peptic Ulcers (PU), typically identified via endoscopy, and Diabetic Foot Ulcers (DFU), identified via external photographic imaging. The proposed model, a fine-tuned Convolutional Neural Network (CNN) architecture (e.g., ResNet-50 or VGG-16), is designed to achieve high diagnostic accuracy, thereby reducing the burden on clinicians and enabling timely intervention. We evaluate the model's performance on standard datasets, achieving a high degree of accuracy, precision, and recall for ulcer identification. Case studies are made for perfection of detection. The findings demonstrate the immense potential of AI in augmenting clinical diagnostic capabilities for diverse ulcer types.