Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network

Muhammad Rizwan, Aysha Shabbir, Abdul Rehman Javed, Maryam Shabbir, Thar Baker, Dhiya Al-Jumeily Obe

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)29731 - 29740
Number of pages10
JournalIEEE Access
Publication statusPublished - 21 Feb 2022


  • Deep learning
  • Gaussian convolutional neural network
  • brain tumor classification


Dive into the research topics of 'Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this