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Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis

Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis

Pham Van Hai, Samson Eloanyi Amaechi
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 23
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa47
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MLA

Van Hai, Pham, and Samson Eloanyi Amaechi. "Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis." IJCINI vol.15, no.4 2021: pp.1-23. http://doi.org/10.4018/IJCINI.20211001.oa47

APA

Van Hai, P. & Amaechi, S. E. (2021). Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-23. http://doi.org/10.4018/IJCINI.20211001.oa47

Chicago

Van Hai, Pham, and Samson Eloanyi Amaechi. "Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-23. http://doi.org/10.4018/IJCINI.20211001.oa47

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Abstract

Conventional methods used in brain tumors detection, diagnosis, and classification such as magnetic resonance imaging and computed tomography scanning technologies are unbridged in their results. This paper presents a proposed model combination, convolutional neural networks with fuzzy rules in the detection and classification of medical imaging such as healthy brain cell and tumors brain cells. This model contributes fully on the automatic classification and detection medical imaging such as brain tumors, heart diseases, breast cancers, HIV and FLU. The experimental result of the proposed model shows overall accuracy of 97.6%, which indicates that the proposed method achieves improved performance than the other current methods in the literature such as [classification of tumors in human brain MRI using wavelet and support vector machine 94.7%, and deep convolutional neural networks with transfer learning for automated brain image classification 95.0%], uses in the detection, diagnosis, and classification of medical imaging decision supports.