Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization.
Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large numbe...
| Published in: | Journal of Medical Systems Vol. 41; no. 10; pp. 1 - 21 |
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| Main Authors: | , , , , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125425155&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125425155 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2017 vid: 41 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125425155 125425155 125425155 10.1007/s10916-017-0797-1 125425155 ppf: 1 ppct: 20 formats: fmt: @attributes: type: P tig: atl: Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization. aug: au: Kuppili, Venkatanareshbabu Biswas, Mainak Sreekumar, Aswini Suri, Harman Saba, Luca Edla, Damodar Marinhoe, Rui Sanches, J. Suri, Jasjit affil: Department of Computer Science and Engineering , National Institute of Technology Goa , Farmagudi India sug: subj: Fatty Liver Diagnosis Image Interpretation, Computer Assisted Liver Ultrasonography Risk Assessment Extreme Learning Machines Human Biopsy Algorithms Protocols Neural Networks (Computer) ROC Curve Time Factors Benchmarking Funding Source ab: Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation ( K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original , S4-four splits , S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols ( K2, K3, K5 and K10) and all types of US data sets ( S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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