Open Access

ARTICLE

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Ziquan Zhu¹, Muhammad Attique Khan², Shui-Hua Wang¹, Yu-Dong Zhang^1,*

1 University of Leicester, Leicester, LE1 7RH, UK
2 HITEC University Taxila, Taxila, 47080, Pakistan

* Corresponding Author: Yu-Dong Zhang. Email:

Computers, Materials & Continua 2023, 74(1), 101-111. https://doi.org/10.32604/cmc.2023.030790

Received 01 April 2022; Accepted 20 May 2022; Issue published 22 September 2022

Abstract

Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to the overfitting problem in CNN. In this paper, our team proposes a novel model (RBEBT) for the automatic classification of brain tumors. We use fine-tuned ResNet18 to extract the features of brain tumor images. The RBEBT is different from the traditional CNN models in that the randomized neural network (RNN) is selected as the classifier. Meanwhile, our team selects the bat algorithm (BA) to optimize the parameters of RNN. We use five-fold cross-validation to verify the superiority of the RBEBT. The accuracy (ACC), specificity (SPE), precision (PRE), sensitivity (SEN), and F1-score (F1) are 99.00%, 95.00%, 99.00%, 100.00%, and 100.00%. The classification performance of the RBEBT is greater than 95%, which can prove that the RBEBT is an effective model to classify brain tumors.

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Keywords

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