DCNNBT: A NOVEL DEEP CONVOLUTION NEURAL NETWORK-BASED BRAIN TUMOR CLASSIFICATION MODEL

Mohd Anul Haq, Ilyas Khan, Ahsan Ahmed, Sayed M. Eldin, Ali Alshehri and Nivin A. Ghamry
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Mohd Anul Haq: Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Ilyas Khan: ��Department of Mathematics, College of Science Al-Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Ahsan Ahmed: ��Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Sayed M. Eldin: �Center of Research, Faculty of Engineering, Future University in Egypt, New Cairo 11835, Egypt
Ali Alshehri: �Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia
Nivin A. Ghamry: ��Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-26

Abstract: An early brain tumor diagnosis is crucial for effective and proactive treatment, which improves the patient’s survival rate. In this paper, we propose a novel Deep Convolutional Neural Network for Brain Tumor (DCNNBT), which detects and classifies brain tumors. The key differentiators of this paper are dimension scaling for image resolution, depth of layers, and width of channels with rigorous optimization of the hyperparameters. DCNNBT classifies and detects four types of brain tumors: benign, pituitary, glioma, and meningioma based on axial, coronal, and sagittal–coronal views. The DCNNBT was developed and tested on two public MRI datasets with more than 403,064 images containing four modalities for 872 patients. The performance of DCNNBT was evaluated against six well-established pre-trained deep learning (DL) models, including SE-ResNet-101, SE-ResNet-152, SENet-154, ResNet152V2, EfficientNetB0, and EfficientNetB5, through transfer learning. In the comparison, DCNNBT showed high accuracy of 99.18% for brain tumor classification, significantly higher than the other studies based on the same database.

Keywords: MRI; Brain Tumor; Classification; EfficientNetB0; ResNet152V2 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401023

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