Open Access

ARTICLE

A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

Abdullah A. Asiri¹, Tariq Ali², Ahmad Shaf², Muhammad Aamir², Muhammad Shoaib³, Muhammad Irfan⁴, Hassan A. Alshamrani^1,*, Fawaz F. Alqahtani¹, Osama M. Alshehri⁵

1 Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, 61441, Saudi Arabia
2 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan
3 Department of Informatics and Systems, University of Management and Technology, Lahore, 54000, Pakistan
4 Electrical Engineering Department, College of Engineering, Najran University, Najran, 61441, Saudi Arabia
5 Department of Clinical Laboratory Sciences, Collage of Applied Medical Science, Najran University, Najran, Saudi Arabia

* Corresponding Author: Hassan A. Alshamrani. Email:

Computers, Materials & Continua 2022, 73(2), 3983-4002. https://doi.org/10.32604/cmc.2022.030923

Received 06 April 2022; Accepted 11 May 2022; Issue published 16 June 2022

Abstract

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death. Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue. Radiologists checked the affected tissue in the slice-by-slice manner, which was time-consuming and hectic task. Therefore, auto segmentation of the affected part is needed to facilitate radiologists. Therefore, we have considered a hybrid model that inherits the convolutional neural network (CNN) properties to the support vector machine (SVM) for the auto-segmented brain tumor region. The CNN model is initially used to detect brain tumors, while SVM is integrated to segment the tumor region correctly. The proposed method was evaluated on a publicly available BraTS2020 dataset. The statistical parameters used in this work for the mathematical measures are precision, accuracy, specificity, sensitivity, and dice coefficient. Overall, our method achieved an accuracy value of 0.98, which is most prominent than existing techniques. Moreover, the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

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Keywords

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