Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network.
Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient's life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is...
| Publicado en: | BioMed Research International pp. 1 - 15 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/8/2021
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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=154009392&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154009392 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/8/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154009392 154009392 154009392 10.1155/2021/3365043 154009392 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network. aug: au: Gull, Sahar Akbar, Shahzad Khan, Habib Ullah affil: Riphah College of Computing, Riphah International University, Faisalabad Campus, Faisalabad 38000, Pakistan sug: subj: Brain Neoplasms Diagnosis Automation Magnetic Resonance Imaging Neural Networks (Computer) Diagnosis, Computer Assisted Human Early Detection of Cancer Conceptual Framework Image Processing, Computer Assisted Image Interpretation, Computer Assisted Brain Neoplasms Classification Experimental Studies Descriptive Statistics ab: Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient's life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is based on the fully convolutional neural network (FCNN) and transfer learning techniques. The proposed framework has five stages which are preprocessing, skull stripping, CNN-based tumor segmentation, postprocessing, and transfer learning-based brain tumor binary classification. In preprocessing, the MR images are filtered to eliminate the noise and are improve the contrast. For segmentation of brain tumor images, the proposed CNN architecture is used, and for postprocessing, the global threshold technique is utilized to eliminate small nontumor regions that enhanced segmentation results. In classification, GoogleNet model is employed on three publicly available datasets. The experimental results depict that the proposed method is achieved average accuracies of 96.50%, 97.50%, and 98% for segmentation and 96.49%, 97.31%, and 98.79% for classification of brain tumor on BRATS2018, BRATS2019, and BRATS2020 datasets, respectively. The outcomes demonstrate that the proposed framework is effective and efficient that attained high performance on BRATS2020 dataset than the other two datasets. According to the experimentation results, the proposed framework outperforms other recent studies in the literature. In addition, this research will uphold doctors and clinicians for automatic diagnosis of brain tumor disease. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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