Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-15627-z.pdf
Reference31 articles.
1. Ahmad M et al (2019) Deep belief network modeling for automatic liver segmentation. IEEE Access 7:20585–20595
2. Balagourouchetty L, Pragatheeswaran JK, Pottakkat B, Ramkumar G (2020) GoogLeNet-based ensemble FCNet classifier for focal liver lesion diagnosis. IEEE J Biomed Health Inform 24(6):1686–1694. https://doi.org/10.1109/JBHI.2019.2942774.18
3. Bevilacqua V et al (2017) A novel approach for hepatocellular carcinoma detection and classification based on triphasic CT protocol. In: A novel approach for hepatocellular carcinoma detection and classification based on triphasic CT protocol, pp 1856–1863
4. Bharti P, Mittal D, Ananthasivan R (2018) Preliminary study of chronic liver classification on ultrasound images using an ensemble model. Ultrason Imaging 40(6):357–379. https://doi.org/10.1177/0161734618787447.12
5. Chen D, Huang M, Li W (2019) Knowledge-powered deep breast tumor classification with multiple medical reports. IEEE/ACM Trans Comput Biol Bioinform 18(3):891–901
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