Ulcerative Colitis Detection and Severity Prediction Using a Hybrid Deep Learning Model
TL;DR
This proposed model uniquely combines- Local convolutional features via ResNet-50, Global contextual features via Vision Transformer, and Handcrafted clinical texture descriptors (GLCM + LBP) and addresses the core limitations of single-architecture models that tend to either underfit local texture patterns or miss long-range spatial dependencies.
Abstract
Ulcerative Colitis Detection and Severity Prediction research creates an effective and dependable automated model to identify the severity of colon diseases using Wireless Capsule Endoscopy (WCE) images. Current methods typically use single deep learning models or conventional machine learning models, which do not readily model both fine-grained variations in mucosal texture and global contextual interactions, particularly when applied to small medical data sets. The improvements of generalization were done by data augmentation and training of the model (categorical cross-entropy loss) with optimized hyperparameters. It was applied to the Python platform with the deep learning libraries and tested on the WCE Curated Colon Disease Dataset, comprising 800 images and four severity levels. The suggested method had a precision of 97.5, which is a better performance than the current models. This system has advantages because it offers accurate diagnosis with computer-assisted assistance to gastroenterologists and aids in the early and accurate evaluation of the severity of UC. The proposed model uniquely combines- Local convolutional features via ResNet-50, Global contextual features via Vision Transformer, and Handcrafted clinical texture descriptors (GLCM + LBP). This multi-source feature fusion is reduced via PCA to preserve 95% variance and addresses the core limitations of single-architecture models that tend to either underfit local texture patterns or miss long-range spatial dependencies.