Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.
Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool ba...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 5 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2019
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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=136129198&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136129198 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2019 vid: 43 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136129198 136129198 136129198 10.1007/s10916-019-1228-2 136129198 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Glioma Tumor Grade Identification Using Artificial Intelligent Techniques. aug: au: Ahammed Muneer K. V. Rajendran, V. R. K., Paul Joseph affil: Department of Electrical Engineering, National Institute of Technology Calicut, 673601, Calicut, India sug: subj: Glioma Radiography Glioma Classification Image Processing, Computer Assisted Methods Neoplasm Grading Methods Artificial Intelligence Utilization Human Male Female Adult Middle Age Magnetic Resonance Imaging Neural Networks (Computer) Record Review Algorithms Utilization Software Utilization Sensitivity and Specificity Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool based classifier (Weighted Neighbour Distance using Compound Heirarchy of Algorithms Representing Morphology) and VGG-19 deep convolutional neural network (DNN). For experimentation, DICOM images are collected from reputed government hospital and the proposed intelligent system categorized the tumor into four grades such as low grade glioma, oligodendroglioma, anaplastic glioma and glioblastoma multiform. After preprocessing, features are extracted, optimized and then classified using Windchrm tool where the most significant features are selected on the basis of Fisher score. In the case of DNN classifier, data augmentation is also performed before applying the images into the deep learning network. The performance of the classifiers are analysed with various measures such as accuracy, precision, sensitivity, specificity and F1-score. The results showed reasonably good performance with a maximum classification accuracy of 92.86% for the Wndchrm classifier and 98.25% for VGG-19 DNN classifier. The results are also compared with similar recent works and the proposed system is found to have better performance. 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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