Heterogeneous ensemble with information theoretic diversity measure for human epithelial cell image classification.
In this work, we propose a heterogeneous committee (ensemble) of diverse members (classification approaches) to solve the problem of human epithelial (HEp-2) cell image classification using indirect Immunofluorescence (IIF) imaging. We hypothesize that an ensemble involving different feature represe...
| Published in: | Medical & Biological Engineering & Computing Vol. 59; no. 5; pp. 1035 - 1055 |
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| Main Authors: | , |
| Format: | Journal Article |
| Published: |
Springer Nature
May2021
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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=150260072&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150260072 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2021 vid: 59 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150260072 149822060 150260072 NLM33860445 10.1007/s11517-021-02336-8 NLM33860445 150260072 ppf: 1035 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Heterogeneous ensemble with information theoretic diversity measure for human epithelial cell image classification. aug: au: Gupta, Vibha Bhavsar, Arnav affil: School of Computer and Electrical Engineering, Indian Institute of Technology, Himachal Pradesh, 175005, Mandi, India sug: subj: Epithelial Cells Probability Dempster Practice Behavior Scale Scales ab: In this work, we propose a heterogeneous committee (ensemble) of diverse members (classification approaches) to solve the problem of human epithelial (HEp-2) cell image classification using indirect Immunofluorescence (IIF) imaging. We hypothesize that an ensemble involving different feature representations can enable higher performance if individual members in the ensemble are sufficiently varied. These members are of two types: (1) CNN-based members, (2) traditional members. For the CNN members, we have employed the well-established ResNet, DenseNet, and Inception models, which have distinctive salient aspects. For the traditional members, we incorporate class-specific features which are characterized depending on visual morphological attributes, and some standard texture features. To select the members which are discriminating and not redundant, we use an information theoretic measure which considers the trade-off between individual accuracies and diversity among the members. For all selected members, a compelling fusion required to combine their outputs to reach a final decision. Thus, we also investigate various fusion methods that combine the opinion of the committee at different levels: maximum voting, product, decision template, Bayes, Dempster-Shafer, etc. The proposed method is evaluated using ICPR-2014 data which consists of more images than some previous datasets ICPR-2012 and demonstrate state-of-the-art performance. To check the effectiveness of the proposed methodology for other related datasets, we test our methodology with newly compiled large-scale HEp-2 dataset with 63K cell images and demonstrate comparable performance even with less number of training samples. The proposed method produces 99.80% and 86.03% accuracy respectively when tested on ICPR-2014 and a new large-scale data containing 63K samples. Graphical Abstract Overview of the proposed methodology. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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