Open Access

ARTICLE

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

Muhammad Ali¹, Jamal Hussain Shah¹, Muhammad Attique Khan², Majed Alhaisoni³, Usman Tariq⁴, Tallha Akram⁵, Ye Jin Kim⁶, Byoungchol Chang^7,*

1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
2 Departmnt of Computer Science, HITEC University Taxila, Pakistan
3 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
5 Department of EE, COMSATS University Islamabad, Wah Campus, Pakistan
6 Department of Computer Science, Hanyang University, Seoul, 04763, Korea
7 Center for Computational Social Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Byoungchol Chang. Email:

Computers, Materials & Continua 2022, 73(3), 4501-4518. https://doi.org/10.32604/cmc.2022.030392

Received 25 March 2022; Accepted 17 May 2022; Issue published 28 July 2022

Abstract

Tumor detection has been an active research topic in recent years due to the high mortality rate. Computer vision (CV) and image processing techniques have recently become popular for detecting tumors in MRI images. The automated detection process is simpler and takes less time than manual processing. In addition, the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians. We proposed a new framework for tumor detection as well as tumor classification into relevant categories in this paper. For tumor segmentation, the proposed framework employs the Particle Swarm Optimization (PSO) algorithm, and for classification, the convolutional neural network (CNN) algorithm. Popular preprocessing techniques such as noise removal, image sharpening, and skull stripping are used at the start of the segmentation process. Then, PSO-based segmentation is applied. In the classification step, two pre-trained CNN models, alexnet and inception-V3, are used and trained using transfer learning. Using a serial approach, features are extracted from both trained models and fused features for final classification. For classification, a variety of machine learning classifiers are used. Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent, respectively, whereas average jaccard values are 96.30 percent and 96.57% (Segmentation Results). The results were extended on the same datasets for classification and achieved 99.0% accuracy, sensitivity of 0.99, specificity of 0.99, and precision of 0.99. Finally, the proposed method is compared to state-of-the-art existing methods and outperforms them.

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Keywords

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