Deep Learning-Based CT Imaging for the Diagnosis of Liver Tumor.

Autor: Zhang H; College of Mathematics and Statistics, Southwest University, Chongqing 400715, China., Luo K; Department of Military Logistics, Army Logistic University of PLA, Chongqing 401331, China., Deng R; Department of Military Logistics, Army Logistic University of PLA, Chongqing 401331, China., Li S; College of Artificial Intelligence, Southwest University, Chongqing 400715, China., Duan S; College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jun 16; Vol. 2022, pp. 3045370. Date of Electronic Publication: 2022 Jun 16 (Print Publication: 2022).
DOI: 10.1155/2022/3045370
Abstrakt: The objective of this research was to investigate the application value of deep learning-based computed tomography (CT) images in the diagnosis of liver tumors. Fifty-eight patients with liver tumors were selected, and their CT images were segmented using a convolutional neural network (CNN) algorithm. The segmentation results were quantitatively evaluated using the Dice similarity coefficient (DSC), precision, and recall. All the patients were examined and diagnosed by CT enhanced delayed scan technique, and the CT scan results were compared with the pathological findings. The results showed that the DSC, precision, and recall of the CNN algorithm reached 0.987, 0.967, and 0.954, respectively. The images segmented by the CNN were clearer. The diagnostic result of the examination on 56 cases by CT enhanced delay scanning was consistent with that of pathological diagnosis. According to the result of pathological diagnosis, there were 6 cases with hepatic cyst, 9 with hepatic hemangioma, 12 cases with liver metastasis, 10 cases with hepatoblastoma, 3 cases with focal nodular hyperplasia, and 18 cases with primary liver cancer. The result of CT enhanced delay scanning on 58 patients was consistent with that of pathological diagnosis, and the total diagnostic coincidence rate reached 96.55%. In conclusion, the CNN algorithm can perform accurate and efficient segmentation, with high resolution, providing a more scientific basis for the segmentation of liver tumors in CT images. CT enhanced scanning technology has a good effect on the diagnosis and differentiation of liver tumor patients, with high diagnostic coincidence rate. It has important value for the diagnosis of liver tumor and is worthy of clinical application.
Competing Interests: The authors declare no conflicts of interest.
(Copyright © 2022 Heng Zhang et al.)
Databáze: MEDLINE
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