Open Access

ARTICLE

An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

Ahsan Aziz¹, Muhammad Attique¹, Usman Tariq², Yunyoung Nam^3,*, Muhammad Nazir¹, Chang-Won Jeong⁴, Reham R. Mostafa⁵, Rasha H. Sakr⁶

1 Department of Computer Science, HITEC University, Taxila, Pakistan
2 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
3 Department of ICT Convergence, Soonchunhyang University, Asan, Korea
4 Medical Convergence Research Center, Wonkwang University, Iksan, Korea
5 Department of Information Systems, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
6 Department of Computer Science, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2021, 69(2), 2653-2670. https://doi.org/10.32604/cmc.2021.018606

Received 13 March 2021; Accepted 26 April 2021; Issue published 21 July 2021

Abstract

Owing to technological developments, Medical image analysis has received considerable attention in the rapid detection and classification of diseases. The brain is an essential organ in humans. Brain tumors cause loss of memory, vision, and name. In 2020, approximately 18,020 deaths occurred due to brain tumors. These cases can be minimized if a brain tumor is diagnosed at a very early stage. Computer vision researchers have introduced several techniques for brain tumor detection and classification. However, owing to many factors, this is still a challenging task. These challenges relate to the tumor size, the shape of a tumor, location of the tumor, selection of important features, among others. In this study, we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features. In the proposed framework, initially, a database is normalized in the form of high-grade glioma (HGG) and low-grade glioma (LGG) patients and then two pre-trained deep learning models (ResNet50 and Densenet201) are chosen. The deep learning models were modified and trained using transfer learning. Subsequently, the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models. The selected features are fused using a serial-based approach and classified using a cubic support vector machine. The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8% and 84.6% for HGG and LGG, respectively. The comparison is performed using several classification methods, and it shows the significance of our proposed technique.

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