Zaghloul, A., El-Attar, N., Elharby, A., Awad, W. (2023). An Automatic system to classify MRI brain tumor using Convolutional Neural Network An Automatic system to classify MRI brain tumor using Convolutional Neural Network. test, 4(4), 664-679. doi: 10.21608/ajbas.2023.216710.1158
Aml Omar Zaghloul; Noha E. El-Attar; Ahmed A Elharby; Wael Abd-Elkader Awad. "An Automatic system to classify MRI brain tumor using Convolutional Neural Network An Automatic system to classify MRI brain tumor using Convolutional Neural Network". test, 4, 4, 2023, 664-679. doi: 10.21608/ajbas.2023.216710.1158
Zaghloul, A., El-Attar, N., Elharby, A., Awad, W. (2023). 'An Automatic system to classify MRI brain tumor using Convolutional Neural Network An Automatic system to classify MRI brain tumor using Convolutional Neural Network', test, 4(4), pp. 664-679. doi: 10.21608/ajbas.2023.216710.1158
Zaghloul, A., El-Attar, N., Elharby, A., Awad, W. An Automatic system to classify MRI brain tumor using Convolutional Neural Network An Automatic system to classify MRI brain tumor using Convolutional Neural Network. test, 2023; 4(4): 664-679. doi: 10.21608/ajbas.2023.216710.1158

An Automatic system to classify MRI brain tumor using Convolutional Neural Network An Automatic system to classify MRI brain tumor using Convolutional Neural Network

Alfarama Journal of Basic & Applied Sciences
Volume 4, Issue 4, October 2023, Pages 664-679    PDF (1.77 M)
Document Type: Original Article
DOI: 10.21608/ajbas.2023.216710.1158
Authors
Aml Omar Zaghloul* 1; Noha E. El-Attar2; Ahmed A Elharby3; Wael Abd-Elkader Awad4
1Institute of Quality Studies and Computer science, Ras Al-Bar.
2Faculty of Computers and AI, Benha University, Benha, Egypt
3Vice Dean for Community Service and Environmental Development of Faculty of Computers and Information, Damietta University
4Department of Computer Science, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, Egypt.
Abstract
The brain tumor is regarded as a serious cancerous tumor that if not detected and accurately identified, may lead in the patient's death. Therefore, recent advances in the field of deep learning (DL) have assisted radiologists in diagnosing tumors with high accuracy and speed when compared to manual diagnosis, which requires the radiologist's effort and competence. Oncologists typically perform the initial evaluation of brain tumors using medical imaging techniques such as computerized tomography (CT) and magnetic resonance imaging (MRI). These two medical imaging techniques are commonly used to create highly detailed images of the brain's structure to monitor any changes. A surgical biopsy of the suspected tissue (tumor) is required for a detailed diagnosis by the specialist if the doctor suspects a brain tumor and needs more information about its type. These various techniques in brain tissue imaging have increased image contrast and resolution in recent years, allowing the radiologist to identify even small lesions and thus achieve higher diagnostic accuracy. This research introduced an automatic system using a Convolutional Neural Network (CNN) to classify MRI brain tumor images consisting of various layers, and then selected the best system that achieved an accuracy of 99.6% with different images sizes and learning rates.
Keywords
Brain tumor; Convolutional neural network; Deep Learning
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