Analysis of high-resolution reconstruction of medical images based on deep convolutional neural networks in lung cancer diagnostics. (April 2022)

• Record Type: Journal Article
• Authors: Bai, Yang; Li, Dan; Duan, Qiongyu; Chen, Xiaodong
• Is Part Of: Computer methods and programs in biomedicine. Volume 217(2022)
• Publication Date: 2022-04
• Subjects: Deep learning -- Convolutional neural network -- 64-Row spiral -- MRI -- Lung 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.2021.106592
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The diagnostic effect of 64-slice spiral CT and MRI high-resolution images based on deep CNN in lung cancer is studied. 74 patients with highly suspected lung cancer were selected as the research objects. The enhanced 64-slice spiral CT and MRI were used to detect and diagnose respectively, and the images and accuracy of CT diagnosis and MRI diagnosis were retrospectively analyzed. The accuracy of CT diagnosis is 94.6% (70/74), and the accuracy of MRI diagnosis is 89.2% (66/74). Enhanced CT and MRI examinations based on CNN to improve image clarity have high application value in the diagnosis of lung cancer. Abstract: Background and objective: To study the diagnostic effect of 64-slice spiral CT and MRI high-resolution images based on deep convolutional neural networks(CNN) in lung cancer. Methods: In this paper, we Select 74 patients with highly suspected lung cancer who were treated in our hospital from January 2017 to January 2021 as the research objects. The enhanced 64-slice spiral CT and MRI were used to detect and diagnose respectively, and the images and accuracy of CT diagnosis and MRI diagnosis were retrospectively analyzed. Results: The accuracy of CT diagnosis is 94.6% (70/74), and the accuracy of MRI diagnosis is 89.2% (66/74). CT examination has the advantages of non-invasive, convenient operation and fast examination. MRI is showing there are advantages in the relationship between the chest wall and the mediastinum, and the relationship between the lesion and the large blood vessels. Conclusion: Enhanced CT and MRI examinations based on convolutional neural networks(CNN) to improve image clarity have high application value in the diagnosis of lung cancer patients, but the focus of performance is different.