A framework for efficient brain tumor classification using MRI images

• Published in: Mathematical biosciences and engineering : MBE Vol. 18; no. 5; pp. 5790 - 5815
• Main Authors: Guan, Yurong, Aamir, Muhammad, Rahman, Ziaur, Ali, Ammara, Abro, Waheed Ahmed, Dayo, Zaheer Ahmed, Bhutta, Muhammad Shoaib, Hu, Zhihua
• Format: Journal Article
• Language: English
• Published: AIMS Press 01.01.2021
• Subjects: brain tumor classification; contrast enhancement; data augmentation; deep learning; deep learning features; healthcare; high-quality locations; mri images; non-linear stretching
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2021292

A brain tumor is an abnormal growth of brain cells inside the head, which reduces the patient's survival chance if it is not diagnosed at an earlier stage. Brain tumors vary in size, different in type, irregular in shapes and require distinct therapies for different patients. Manual diagnosis of brain tumors is less efficient, prone to error and time-consuming. Besides, it is a strenuous task, which counts on radiologist experience and proficiency. Therefore, a modern and efficient automated computer-assisted diagnosis (CAD) system is required which may appropriately address the aforementioned problems at high accuracy is presently in need. Aiming to enhance performance and minimise human efforts, in this manuscript, the first brain MRI image is pre-processed to improve its visual quality and increase sample images to avoid over-fitting in the network. Second, the tumor proposals or locations are obtained based on the agglomerative clustering-based method. Third, image proposals and enhanced input image are transferred to backbone architecture for features extraction. Fourth, high-quality image proposals or locations are obtained based on a refinement network, and others are discarded. Next, these refined proposals are aligned to the same size, and finally, transferred to the head network to achieve the desired classification task. The proposed method is a potent tumor grading tool assessed on a publicly available brain tumor dataset. Extensive experiment results show that the proposed method outperformed the existing approaches evaluated on the same dataset and achieved an optimal performance with an overall classification accuracy of 98.04%. Besides, the model yielded the accuracy of 98.17, 98.66, 99.24%, sensitivity (recall) of 96.89, 97.82, 99.24%, and specificity of 98.55, 99.38, 99.25% for Meningioma, Glioma, and Pituitary classes, respectively.