A framework for efficient brain tumor classification using MRI images

Mathematical Biosciences and Engineering

卷:18

Zaheer Ahmed Dayo¹  Muhammad Aamir¹  Yurong Guan¹  Waheed Ahmed Abro¹  Ziaur Rahman¹  Zhihua Hu¹  Ammara Ali²  Muhammad Shoaib Bhutta³ 

[1] 1. Department of Computer Science, Huanggang Normal University, Huangzhou 438000, China;

[2] 2. Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Norway;

[3] 3. Binjiang College, Nanjing University of Information Science & Technology, Wuxi 214105, China;

关键词: brain tumor classification;    contrast enhancement;    non-linear stretching;    deep learning features;    data augmentation;    high-quality locations;    mri images;    deep learning;    healthcare;   

DOI  :  10.3934/mbe.2021292

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown