Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound.
A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellu...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 4; pp. 520 - 538 |
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| Autores principales: | , , , |
| Formato: | diagnostic images glossary research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=96967531&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 96967531 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2014 vid: 27 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 96967531 96967531 96967531 10.1007/s10278-014-9685-0 96967531 ppf: 520 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound. aug: au: Virmani, Jitendra Kumar, Vinod Kalra, Naveen Khandelwal, Niranjan affil: Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Solan 173234 Himachal Pradesh India sug: subj: Liver Diseases Ultrasonography Diagnosis, Differential Diagnosis, Computer Assisted Neural Networks (Computer) Liver Diseases Classification Human Experimental Studies Funding Source ab: A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs. pubtype: Academic Journal doctype: diagnostic images glossary research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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