Developing an Artificial Intelligence Model for Tumor Grading and Classification, Based on MRI Sequences of Human Brain Gliomas.
Background: Artificial intelligence (AI) models have provided advanced applications to many scientific areas, including the prediction of the pathologic grade of tumors, utilizing radiology techniques. Gliomas are among the malignant brain tumors in human adults, and their efficient diagnosis is of...
| Publicado en: | International Journal of Cancer Management Vol. 15; no. 1; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Medical Journals Commission of the Ministry of Health & Medical Education
Jan2022
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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=155359971&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155359971 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 25384422 L6AI jtl: International Journal of Cancer Management issn: 25384422 maglogo: N pubinfo: dt: Jan2022 vid: 15 iid: 1 pid: 66482 pub: Medical Journals Commission of the Ministry of Health & Medical Education artinfo: ui: 155359971 155359971 155359971 10.5812/ijcm.120638 155359971 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Developing an Artificial Intelligence Model for Tumor Grading and Classification, Based on MRI Sequences of Human Brain Gliomas. aug: au: Khazaee, Zeinab Langarizadeh, Mostafa Shiri Ahmadabadi, Mohammad Ebrahim affil: Department of Information Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran sug: subj: Artificial Intelligence Neoplasm Grading Glioma Classification Magnetic Resonance Imaging Image Interpretation, Computer Assisted Methods Human Iran Neural Networks (Computer) Deep Learning ab: Background: Artificial intelligence (AI) models have provided advanced applications to many scientific areas, including the prediction of the pathologic grade of tumors, utilizing radiology techniques. Gliomas are among the malignant brain tumors in human adults, and their efficient diagnosis is of high clinical significance. Objectives: Given the contribution of AI to medical diagnoses, we investigated the role of deep learning in the differential diagnosis and grading of human brain gliomas. Methods: This study developed a new AI diagnostic model, i.e., EfficientNetB0, to grade and classify human brain gliomas, using sequences from magnetic resonance imaging (MRI). Results: We validated the newAI model, using a standard dataset (BraTS-2019) and demonstrated that the AI components, i.e., convo-lutional neural networks and transfer learning, provided excellent performance for classifying and grading glioma images at 98.8% accuracy. Conclusions: The proposed model, EfficientNetB0, is capable to classify and grade glioma from MRI sequences at high accuracy, validity, and specificity. It can provide better performance and diagnostic results for human glioma images than models developed by previous studies. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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