Copyright
©The Author(s) 2019.
World J Gastrointest Oncol. Dec 15, 2019; 11(12): 1218-1230
Published online Dec 15, 2019. doi: 10.4251/wjgo.v11.i12.1218
Published online Dec 15, 2019. doi: 10.4251/wjgo.v11.i12.1218
Population | Worldwide, datasets, patients, males and females, humans |
Intervention | Use of CNN-diagnostic proposals and computer models |
Comparison | Normal population, use of manual detection by radiologists, hepatologists, or anatomical pathologists examining images/slides. |
Outcome | Lesion detection, classification, segmentation, or image reconstruction. |
Studies | Controlled, or comparison to manual (routine) assessment, or comparison to benchmark or other artificial intelligence models. |
- Citation: Azer SA. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World J Gastrointest Oncol 2019; 11(12): 1218-1230
- URL: https://www.wjgnet.com/1948-5204/full/v11/i12/1218.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v11.i12.1218