Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks.
Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learnin...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 1; pp. 57 - 71 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2021
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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=148139437&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148139437 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2021 vid: 59 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148139437 147212715 148139437 NLM33222016 10.1007/s11517-020-02290-x NLM33222016 148139437 ppf: 57 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks. aug: au: Toğaçar, Mesut Ergen, Burhan Cömert, Zafer affil: Department of Computer Technology, Technical Sciences Vocational School, Fırat University, Elazig, Turkey sug: subj: Postoperative Complications Image Processing, Computer Assisted Attention Brain Scales ab: Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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