A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning.
Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth inc...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 11; pp. 1 - 19 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2019
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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=142115170&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142115170 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2019 vid: 43 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142115170 10.1007/s10916-019-1453-8 142115170 ppf: 1 ppct: 18 formats: fmt: @attributes: type: P tig: atl: A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning. aug: au: Amin, Javeria Sharif, Muhammad Yasmin, Mussarat Saba, Tanzila Anjum, Muhammad Almas Fernandes, Steven Lawrence affil: Department of Computer Science, University of Wah, Wah, Pakistan sug: ab: Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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