The effect of CT high-resolution imaging diagnosis based on deep residual network on the pathology of bladder cancer classification and staging. (March 2022)

• Record Type: Journal Article
• Authors: Liu, Dongmei; Wang, Shubao; Wang, Jing
• Is Part Of: Computer methods and programs in biomedicine. Volume 215(2022)
• Publication Date: 2022-03
• Subjects: Residual network -- Deep learning -- Super-resolution processing -- Non-local -- CT -- Staging of bladder cancer; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106635
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developed an effective deep residual network to efficiently and accurately predict the staging diagnosis of bladder tumors. Combined with the image super-resolution processing and non-local attention mechanism. Established a model of ResNet structure combined with non-Local attention mechanism. The proposed the deep residual network has good performance than the state-of-the-art methods. Abstract: Background and objective: To study the high-resolution CT image based on deep residual network to efficiently and accurately predict the staging diagnosis of bladder tumors. Methods: The image was processed with super-resolution to restore the missing details of the image. The CT data of 75 bladder patients who were treated in our hospital from June to December 2013 were collected. And obtain the patient's classification and staging information through pathology, which is used to establish a model of ResNet structure combined with non-Local attention mechanism. The clinical data of 76 patients with bladder disease admitted to our hospital from May 2018 to August 2021 were randomly selected, and the imaging and accuracy of CT diagnosis were retrospectively analyzed. Results: 52 cases were diagnosed <T1 stage, 16 cases belonged to T2 stage, 2 cases T3 stage, and 2 cases T4 stage. The sensitivity rate of experimental diagnosis was 94.74%, which was not significantly different from the sensitivity rate of preoperative pathological diagnosis. Conclusion: CT based on deep residual network has high application value in the diagnosis and staging of bladder cancer, can effectively improve the diagnostic accuracy, and is worthy of clinical application.