Hierarchal Bayes model with AlexNet for characterization of M-FISH chromosome images.
The analysis of chromosomes is a significant and challenging task for clinical diagnosis and biological research. The technique based on color imaging is a multiplex fluorescent in situ hybridization (M-FISH), which was implemented to ease the exploration of the chromosomes. Thus, in this paper, we...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 7/8; pp. 1529 - 1545 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2021
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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=151585391&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151585391 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2021 vid: 59 iid: 7/8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151585391 151300840 151585391 NLM34232447 10.1007/s11517-021-02384-0 NLM34232447 151585391 ppf: 1529 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Hierarchal Bayes model with AlexNet for characterization of M-FISH chromosome images. aug: au: Kanimozhi, V. S. Balasubramani, M. Anuradha, R. affil: Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan College of Engineering, Coimbatore, Tamil Nadu, India sug: subj: Chromosomes Algorithms In Situ Hybridization, Fluorescence Diagnostic Imaging Probability Image Processing, Computer Assisted Scales ab: The analysis of chromosomes is a significant and challenging task for clinical diagnosis and biological research. The technique based on color imaging is a multiplex fluorescent in situ hybridization (M-FISH), which was implemented to ease the exploration of the chromosomes. Thus, in this paper, we propose a novel quasi-Newton-based K-means clustering for the M-FISH image segmentation. Then, we use the expectation-maximization-based hierarchical Bayes model to characterize the M-FISH images. The contextual-based classification and region merging of chromosomal images is made to avoid any misclassification, and we made use of AlexNet, by modifying the activation functions of the sigmoid and softmax layer and for the optimum classification between the autosomal chromosomes and the sex chromosome. Finally, we conducted a performance analysis by measuring accuracy, recall, sensitivity, specificity, PPV, NPV, F-score, kappa, Jaccard, and Dice coefficient and compared with other existing methods and found that our proposed methodology can achieve more percentage of accuracy (6.96%) than the state of the art methods. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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