Open Access

ARTICLE

Early-Stage Segmentation and Characterization of Brain Tumor

Syed Nauyan Rashid¹, Muhammad Hanif^2,*, Usman Habib², Akhtar Khalil³, Omair Inam⁴, Hafeez Ur Rehman¹

1 Department of Computer Science, National University of Computer and Emerging Sciences Islamabad, Peshawar Campus, Pakistan
2 Faculty of Computer Science and Engineering, Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology, Topi, Pakistan
3 IFahja Pvt Limited, Peshawar, Pakistan
4 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan

* Corresponding Author: Muhammad Hanif. Email:

Computers, Materials & Continua 2022, 73(1), 1001-1017. https://doi.org/10.32604/cmc.2022.023135

Received 29 August 2021; Accepted 17 January 2022; Issue published 18 May 2022

Abstract

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain tissues. The life expectancy of patients diagnosed with gliomas decreases exponentially. Most gliomas are diagnosed in later stages, resulting in imminent death. On average, patients do not survive 14 months after diagnosis. The only way to minimize the impact of this inevitable disease is through early diagnosis. The Magnetic Resonance Imaging (MRI) scans, because of their better tissue contrast, are most frequently used to assess the brain tissues. The manual classification of MRI scans takes a reasonable amount of time to classify brain tumors. Besides this, dealing with MRI scans manually is also cumbersome, thus affects the classification accuracy. To eradicate this problem, researchers have come up with automatic and semi-automatic methods that help in the automation of brain tumor classification task. Although, many techniques have been devised to address this issue, the existing methods still struggle to characterize the enhancing region. This is because of low variance in enhancing region which give poor contrast in MRI scans. In this study, we propose a novel deep learning based method consisting of a series of steps, namely: data pre-processing, patch extraction, patch pre-processing, and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing region. Our trained model achieved better results for all glioma classes including the enhancing region. The improved performance of our technique can be attributed to several factors. Firstly, the non-local mean filter in the pre-processing step, improved the image detail while removing irrelevant noise. Secondly, the architecture we employ can capture the non-linearity of all classes including the enhancing region. Overall, the segmentation scores achieved on the Dice Similarity Coefficient (DSC) metric for normal, necrosis, edema, enhancing and non-enhancing tumor classes are 0.95, 0.97, 0.91, 0.93, 0.95; respectively.

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Keywords

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