Enhancing Ulcerative Colitis Diagnosis: A Multi-Level Classification Approach with Deep Learning

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis, a condition with significant clinical implications. However, endoscopic assessment is susceptible to inherent variations, both within and between observers, compromising the reliability of individual evaluations. This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity. To initiate this endeavor, a multi-faceted approach is embarked upon. The dataset is meticulously preprocessed, enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization (CLAHE). A diverse array of data augmentation techniques, encompassing various geometric transformations, is leveraged to fortify the dataset's diversity and facilitate effective feature extraction. A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles, harnessing a modified ResNet-50 architecture. This augmentation, informed by domain expertise, contributed significantly to enhancing the model's classification performance. The outcome of this research endeavor yielded a highly promising model, demonstrating an accuracy rate of 86.85%, coupled with a recall rate of 82.11% and a precision rate of 89.23%.