detection and diagnosis of ulcerative colitis in endoscopy and colonoscopy images using deep learning models

Abstract

In the contemporary world, the prevalence of diseases impacting human newlinelife is influenced by various factors such as dietary habits, stress levels, and newlinegenetic predisposition. Among these diseases, Ulcerative Colitis (UC) stands newlineas a chronic inflammatory bowel disease that affects a substantial number of newlineindividuals worldwide. UC is characterized by inflammation and ulcers in the newlinecolon and rectum, resulting from a combination of biological disposition, newlineenvironmental exposures, and dysregulated immune reactions. However, newlinediagnosing UC accurately poses challenges due to its diverse traits and patterns. newlineIn order to solve this, the current research aims to find UC remissions by using newlinecomputational algorithms. newlineThe primary aim of this work is to determine the contributing factors to newlineUC and to develop a comprehensive step-by-step diagnostic system. By newlineutilizing a medical dataset and leveraging Convolutional Neural Network newline(CNN) techniques, essential features are extracted, and different stages of UC newlineare classified to facilitate appropriate medication. The research highlights the newlinecrucial role of endoscopy and colonoscopy procedures in the effective newlinediagnosis of UC. These procedures enable healthcare professionals to visually newlineexamine the upper digestive system and the inner lining of the colon, aiding in newlinethe identification of UC-related abnormalities. By integrating computational newlinealgorithms and advanced imaging techniques, this research aims to enhance the newlineaccuracy and efficiency of UC diagnosis. newline