Analysis of Ulcerative Colitis Detection from Encoscopic Video

In this work, endoscopic video analysis is used to diagnose ulcerative colitis by combining coarse-grained and finegrained information. Motivated by the progress in detecting malignancies from microscopic pictures, we investigate the effectiveness of deep learning models, including sophisticated models like MobileNet-V3, Inception-V3, and EfficientNet-B0. We show that these models are effective in classifying various forms of ulcerative colitis, which holds promise for improved sensitivity, specificity, and accuracy in automated disease detection systems. Drawing on a comprehensive examination of sensitivity and specificity, we emphasize how these models frequently strike a compromise between a low probability of false positives and negatives and fewer negative and more positive identifications. The relevance of precise disease forecasts and customized therapies for specific ulcerative colitis subtypes are underscored by these advanced findings. Our findings indicate that reliable techniques for diagnosing ulcerative colitis have the potential to completely transform the medical field. Because of the increased precision in sickness evaluation that these advancements provide, they may enhance diagnostic accuracy and mitigate the consequences of delayed or incorrect diagnosis. Our work improves the healthcare elements through better diagnostic apps for ulcerative colitis and insights into sustainable procedures.