Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S (2007). Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artificial intelligence in medicine, 41(1), pp. 25-37. Amsterdam: Elsevier 10.1016/j.artmed.2007.05.002
Full text not available from this repository. (Request a copy)The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition |
UniBE Contributor: |
Mougiakakou, Stavroula |
ISSN: |
0933-3657 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Factscience Import |
Date Deposited: |
04 Oct 2013 15:23 |
Last Modified: |
05 Dec 2022 14:25 |
Publisher DOI: |
10.1016/j.artmed.2007.05.002 |
PubMed ID: |
17624744 |
Web of Science ID: |
000249821200003 |
URI: |
https://boris.unibe.ch/id/eprint/37258 (FactScience: 207282) |
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