AFR: An image-aided diagnostic approach for ulcerative colitis

Ulcerative Colitis (UC) is a chronic inflammatory bowel disease, and its diagnosis and evaluation mainly rely on colonoscopy. Aiming to improve the accuracy of UC early diagnosis, this study proposes a novel deep learning model, Attention-Focused Refinement (AFR), to assist in the accurate classification of UC colonoscopy images. The AFR model combines advanced attention mechanisms and feature refinement technology to enhance the classification performance of UC enteroscopy images through a self-supervised learning strategy and a multi-module integration method. The model design takes into account the characteristics of UC colonoscopy images, focuses on analyzing the subtle features of the lesion area, and reduces sensitivity to interference factors. The model was trained on the UC colonoscopy image dataset and evaluated against existing models. The experimental results demonstrated that the AFR model exhibited high accuracy in the four Mayo score categories (0 points normal, 1 points mild, 2 points moderate, and 3 points severe) of UC colonoscopy images, with accuracies of 0.996, 0.992, 0.972, and 0.994, respectively. These results were validated on independent datasets, showcasing the reliability and effectiveness of the AFR model in various clinical settings. Ablation experiments and comparative analyses with other state-of-the-art models further confirm the applicability and stability of the AFR model in classification tasks. The AFR model shows promising results in UC colonoscopy image classification task, validating its effectiveness as an auxiliary diagnostic tool. This model introduces new possibilities to enhance the accuracy and efficiency of UC diagnosis. • We propose the AFR method specifically for classifying Ulcerative Colitis colonoscopy images. • Attention-Guided Feature Refinement Module optimize the handling of interference factors. • Local Subtle Feature Identification Module can accurately identify small lesions. • Cosine Similarity Quantification Module enhances the analysis of features in the lesion area. • Our method achieves over 97% accuracy in classifying Mayo scores.