A Computer-Aided Diagnosis System for Ulcerative Colitis Classification Using Vision Transformer

An unhealthy digestive condition that inflames the colon is called ulcerative colitis (UC). Utilising colonoscopy information to assess disease severity is a laborious process that concentrates on the most severe anomalies. The severity of this condition can significantly impact a patient’s quality of life. Current diagnostic methods, primarily colonoscopy, for assessing UC severity are subjective and prone to inter-observer variability, hindering accurate staging and personalized treatment. Colonoscopies are currently used by doctors to diagnose the severity of ulcerative colitis, yet this might be imprecise due to physician variance. As such, to deliver optimal outcomes, automated and precise technology is required. The current study introduces UC-visionNet, an automated approach that classifies ulcerative colitis severity based on colonoscopy image analysis using vision transfer techniques. UC-visionNet makes use of vision transformers, which are pre-trained deep learning models that have shown to be quite successful in image analysis applications. To classify ulcerative colitis severity, these models are “fine-tuned” using the LIMUC (Labeled Images for Ulcerative Colitis) dataset. Compared to conventional colonoscopy procedures, using UC-visionNet for image analysis may be faster, enhancing patient satisfaction and increasing healthcare effectiveness. In contrast to state-of-the-art techniques, the suggested model performs quantitatively better on the LIMUS dataset. After using Vision transformer (ViT) on the LIMUC dataset, the current study attained a 96% training accuracy. UC-visionNet offers a promising automated solution for accurate and efficient UC severity classification.