Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification.
Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep mu...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 4; pp. 903 - 916 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2020
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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=146122226&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146122226 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2020 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146122226 144086423 146122226 146122226 10.1007/s10278-020-00347-9 146122226 ppf: 903 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification. aug: au: Mzoughi, Hiba Njeh, Ines Wali, Ali Slima, Mohamed Ben BenHamida, Ahmed Mhiri, Chokri Mahfoudhe, Kharedine Ben affil: Advanced Technologies for Medecine and Signal (ATMS), Sfax university, ENIS, Route de la Soukra km 4, 3038, Sfax, Tunisia sug: subj: Glioma Diagnosis Glioma Classification Brain Neoplasms Classification Magnetic Resonance Imaging Methods Neural Networks (Computer) Methods Neuroradiography Neoplasm Grading Deep Learning ab: Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy. Quantitative evaluations, over the well-known benchmark (Brats-2018), attest that the proposed architecture generates the most discriminative feature map to distinguish between LG and HG gliomas compared with 2D CNN variant. The proposed approach offers promising results outperforming the recently supervised and unsupervised state-of-the-art approaches by achieving an overall accuracy of 96.49% using the validation dataset. The obtained experimental results confirm that adequate MRI's preprocessing and data augmentation could lead to an accurate classification when exploiting CNN-based approaches. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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