Feshawy, S., Saad, W., Shokair, M., Dessouky, M. (2022). Proposed Approaches for Brain Tumors Detection Techniques Using Convolutional Neural Networks. test, 02(01), 1-14. doi: 10.21608/ijt.2022.266293
Somaya Feshawy; Waleed Saad; Mona Shokair; Moawad Dessouky. "Proposed Approaches for Brain Tumors Detection Techniques Using Convolutional Neural Networks". test, 02, 01, 2022, 1-14. doi: 10.21608/ijt.2022.266293
Feshawy, S., Saad, W., Shokair, M., Dessouky, M. (2022). 'Proposed Approaches for Brain Tumors Detection Techniques Using Convolutional Neural Networks', test, 02(01), pp. 1-14. doi: 10.21608/ijt.2022.266293
Feshawy, S., Saad, W., Shokair, M., Dessouky, M. Proposed Approaches for Brain Tumors Detection Techniques Using Convolutional Neural Networks. test, 2022; 02(01): 1-14. doi: 10.21608/ijt.2022.266293

Proposed Approaches for Brain Tumors Detection Techniques Using Convolutional Neural Networks

International Journal of Telecommunications
Volume 02, Issue 01 - Serial Number 2, July 2022, Pages 1-14    PDF (2.32 M)
Document Type: Original Article
DOI: 10.21608/ijt.2022.266293
Authors
Somaya Feshawy* 1; Waleed Saad1; Mona Shokair2; Moawad Dessouky3
1Electronic and Electrical Communication Department, Faculty of Electronic Engineering, Menoufia University
2Electronic and Electrical Communication Department, Faculty of Electronic Engineering, Menoufia University,
3Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufiya University
Abstract
A brain tumor is an intracranial mass consisting of irregular growth of brain tissue cells. Medical imaging plays a vital role in discovering and examining the precise performance of organs The performance of object detection has increased dramatically by taking advantage of recent advances in deep learning. This paper presents a Convolu-tional Neural Network (CNN) architecture model-based classification approach for brain tumor detection from Magnetic Resonance Imaging (MRI) images. The network training was carried out in both the original dataset and the augmented dataset. Whereas the whole brain MRI images were scaled to fit the input image size of each pre-trained CNN network. Moreover, a comparative study between the proposed model and other pre-trained models was made in terms of accuracy, precision, specificity, sensitivity, and F1-score. Finally, experimental results reveal that without data augmentation, the pro-posed approach achieves an overall accuracy rate of 96.35 percent for a split ratio of 80:20. While the addition of data augmentation boosted the accuracy to 97.78 percent for the same split ratio. Thus, the obtained results demonstrate the effectiveness of the proposed approach to assist professionals in Automated medical diagnostic services
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