Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification
Open accessAbstract
This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Transfer learning strategy is frequently adopted for liver cancer US diagnosis.
“In addition, the transfer learning strategy is frequently adopted for liver cancer US diagnosis [58,94].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [58]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 25 · Cited by 119
Research works printed but not linked: 1
- [8]Krishnan K.R. Sudhakar R. Automatic classification of liver diseases from ultrasound images using GLRLM texture features Soft Computing Applications Springer Berlin, Germany 2013 611 624
- Authors
- Liu, X., Song, J., Wang, S., Zhao, J., & Chen, Y.
- Affiliations
- Shanghai University of Engineering Science (CN)
- Second Military Medical University (CN)
- Fudan University (CN)
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China
- Corresponding author
- Chen, Yan
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-01-13
- Volume
- 17
- Issue
- 1
- Pages
- 149
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5298722
- OpenAlex
- W2578388616
- Cited by
- 119
- References
- 25
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 6 · 2023: 23 · 2022: 18 · 2021: 23 · 2020: 15 · 2019: 11 · 2018: 8 · 2017: 6
- FWCI
- 4.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61175036)
- Youth National Natural Science Foundation of China (81101105)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- liver cirrhosis, computer-aided diagnosis, ultrasound image classification, liver ultrasound, support vector machine, liver disease detection
- MeSH terms
- Humans, Liver Cirrhosis, Ultrasonography, Neural Networks, Computer, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
