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Brain Tumor Detection and Segmentation Using RCNN

Maham Khan1, Syed Adnan Shah1, Tenvir Ali2, Quratulain2, Aymen Khan2, Gyu Sang Choi3,*

1 Department of Computer Science, University of Engineering & Technology, Taxila, 47080, Pakistan
2 Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
3 Department of Information and Communication Engineering, Yeungnam University, Gyeongbuk, 38541, Korea

* Corresponding Author: Gyu Sang Choi. Email: email

(This article belongs to the Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)

Computers, Materials & Continua 2022, 71(3), 5005-5020. https://doi.org/10.32604/cmc.2022.023007

Abstract

Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images, which helps solve the difficulties of brain tumor identification efficiently and accurately. Our methodology is based on the accurate and efficient selection of tumorous areas. That reduces computational complexity and time. We have validated the designed experimental setup on a standard dataset, BraTS 2020. We used binary evaluation matrices based on Dice Similarity Coefficient (DSC) and Mean Average Precision (mAP). The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method. The suggested approach attained an average DSC of 0.92 and mAP 0.92 for 10 patients, while on the whole dataset, the scores are DSC 0.89 and mAP 0.90. The following results clearly show the performance efficiency of the proposed methodology.

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Cite This Article

APA Style
Khan, M., Shah, S.A., Ali, T., Quratulain, , Khan, A. et al. (2022). Brain tumor detection and segmentation using RCNN. Computers, Materials & Continua, 71(3), 5005-5020. https://doi.org/10.32604/cmc.2022.023007
Vancouver Style
Khan M, Shah SA, Ali T, Quratulain , Khan A, Choi GS. Brain tumor detection and segmentation using RCNN. Comput Mater Contin. 2022;71(3):5005-5020 https://doi.org/10.32604/cmc.2022.023007
IEEE Style
M. Khan, S.A. Shah, T. Ali, Quratulain, A. Khan, and G.S. Choi "Brain Tumor Detection and Segmentation Using RCNN," Comput. Mater. Contin., vol. 71, no. 3, pp. 5005-5020. 2022. https://doi.org/10.32604/cmc.2022.023007



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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