UCFNNet: Ulcerative colitis evaluation based on fine-grained lesion learner and noise suppression gating

• Published in: Computer methods and programs in biomedicine 2024-04, Vol.247, p.108080-108080, Article 108080
• Main Authors: Li, Haiyan, Wang, Zhixin, Guan, Zheng, Miao, Jiarong, Li, Weihua, Yu, Pengfei, Molina Jimenez, Carlos
• Format: Article
• Language: English
• Subjects: Deep learning; Fine-grained lesion feature learner; Noise suppression gating; Ulcerative colitis evaluation
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2024.108080

•The highlights of this article are mainly divided into the following aspects:.•Firstly, a fine-grained lesion feature learner (FG-LF Learner) is proposed by integrating local features and a Softmax category prediction (SCP) module to improve the feature accuracy in small lesion areas, thus addressing the challenge of detecting small lesions.•Subsequently, a graph convolutional feature combiner (GCFC) is developed to connect features across adjacent convolutional layers and to incorporate short connections between input and output, thereby improving the network's feature transmission capability and mitigating the problem of feature loss during transmission.•Thereafter, a noise suppression gating (NS gating) technique is designed by implementing a grid attention mechanism and a feature gating (FG) module to prioritize significant lesion features and suppress irrelevant and noisy regions in the input feature map.•Finally, we employed gradient-weighted class activation mapping (Grad-CAM) technique generate a localization map for identifying and suspicious lesions, facilitating an intuitive understanding and explanation of the model's effectiveness.•We evaluated the performance of the proposed network on both privately-collected and publicly-available datasets. The evaluation of UC achieves excellent results on privately-collected dataset, with an accuracy (ACC) of 89.57 %, Matthews correlation coefficient (MCC) of 85.52 %, precision of 89.26 %, recall of 89.48 %, and F1-score of 89.78 %. On publicly-available dataset, the results are also impressive, with ACC of 85.47 %, MCC of 80.42 %, precision of 85.62 %, recall of 84.00 %, and F1-score of 84.53 %, surpassing the performance of state-of-the-art techniques. Ulcerative colitis (UC) is a chronic disease characterized by recurrent symptoms and significant morbidity. The exact cause of the disease remains unknown. The selection of current treatment options for ulcerative colitis depends on the severity and location of the disease in each patient. Therefore, developing a fully automated endoscopic images for evaluating UC is crucial for guiding treatment plans and facilitating early prevention efforts. We propose a network called ulcerative colitis evaluation based on fine-grained lesion learner and noise suppression gating (UCFNNet). UCFNNet contains three novel modules. Firstly, a fine-grained lesion feature learner (FG-LF Learner) is proposed by integrating local features and a Softmax category prediction (SC