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Rizwan, M, Shabbir, A, Javed, AR, Shabbir, M, Baker, T and Al-Jumeily, D (2022) Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network. IEEE Access, 10. pp. 29731-29740.
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Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Understanding brain diseases such as categorizing Brain-Tumor (BT) is critical to assess the tumors and facilitate the patient with proper cure as per their categorizations. Numerous imaging schemes exist for BT detection, such as Magnetic Resonance Imaging (MRI), generally utilized because of the better quality of images and the reality of depending on non-ionizing radiation. This paper proposes an approach to detect distinctive BT types using Gaussian Convolutional Neural Network (GCNN) on two datasets. One of the datasets is used to classify tumors into pituitary, glioma, and meningioma. The other one separates the three grades of glioma, i.e., Grade-two, Grade-three, and Grade-four. These datasets have '233' and '73' victims with a total of '3064' and '516' images on T1-weighted complexity improved pictures for the first and second datasets, separately. The proposed approach achieves an accuracy of 99.8% and 97.14% for the two datasets. The experimental results highlight the efficiency of the proposed approach for BT multi-class categorization.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 08 Information and Computing Sciences; 09 Engineering; 10 Technology |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 07 Oct 2022 11:44 |
| Last Modified: | 14 Feb 2024 11:00 |
| DOI or ID number: | 10.1109/ACCESS.2022.3153108 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/17755 |
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