Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network

Journal article


Rizwan, M., Aysha, S., Maryam, S., AR Javed, Baker, T. and Dhiya, O. 2022. Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network. IEEE Access. 10, pp. 29731 - 29740. https://doi.org/10.1109/ACCESS.2022.3153108
AuthorsRizwan, M., Aysha, S., Maryam, S., AR Javed, Baker, T. and Dhiya, O.
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.

KeywordsDeep learning; Gaussian convolutional neural network; brain tumor classification,
Year2022
JournalIEEE Access
Journal citation10, pp. 29731 - 29740
PublisherIEEE
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2022.3153108
Web address (URL)https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9718069
Accepted author manuscript
File Access Level
Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online21 Feb 2022
Publication process dates
Accepted14 Feb 2022
Deposited27 Mar 2024
Permalink -

https://repository.derby.ac.uk/item/q566z/brain-tumor-and-glioma-grade-classification-using-gaussian-convolutional-neural-network

Download files

  • 18
    total views
  • 19
    total downloads
  • 0
    views this month
  • 1
    downloads this month

Export as

Related outputs

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems
Abid, R., Rizwan, M., Alabdulatif, A., Alnajim, A., Alamro, M. and Azrour, M. 2024. Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems. CMC-Computers, Materials & Continua. pp. 1-17. https://doi.org/10.32604/cmc.2024.046880
Enhanced clustering based routing protocol in vehicular ad‐hocnetworks
Afia, N., Rizwan, M., Shtwai, A., Almadhor, A., Akhtaruzzaman, Md., Islam, S. and Rahman, H. 2023. Enhanced clustering based routing protocol in vehicular ad‐hocnetworks. IET Electrical Systems in Transportation. 13 (1), pp. 1-15. https://doi.org/10.1049/els2.12069
Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions
Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T.R., Rizwan, M., Maddikunta, P.K.R., Mahmud, M., Liyanage, M and Hussain, A. 2023. Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognitive Computation. pp. 1-46. https://doi.org/10.1007/s12559-023-10153-4
Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network
Alshahrani, H., Khan, A., Rizwan, M., Al Reshan, M. S., Sulaiman, A. and Shaikh, A. 2023. Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network. Sustainability. 15 (11), pp. 1-18. https://doi.org/10.3390/su15119001
Intelligent Transportation Systems in Smart City: A Systematic Survey
Hassan, M. A., Javed, R., Farhatullah, Granelli, F., Gen, X., Rizwan, M., Ali, S. H., Junaid, H. and Ullah, S. 2023. Intelligent Transportation Systems in Smart City: A Systematic Survey. 2023 International Conference on Robotics and Automation in Industry (ICRAI). IEEE Computer Society. https://doi.org/10.1109/ICRAI57502.2023.10089543
Exploratory Data Analysis, Classification, Comparative Analysis, Case Severity Detection, and Internet of Things in COVID 19 Telemonitoring for Smart Hospitals
Shabbir, A., Shabbir, M., Javed, A. R., Rizwan, M., Iwendi, C. and Chakraborty, C. 2022. Exploratory Data Analysis, Classification, Comparative Analysis, Case Severity Detection, and Internet of Things in COVID 19 Telemonitoring for Smart Hospitals. Journal of Experimental & Theoretical Artificial Intelligence. 35 (4), pp. 507-534. https://doi.org/10.1080/0952813X.2021.1960634