Brain tumor detection using CNN, AlexNet &amp; GoogLeNet ensembling learning approaches

Chetan Swarup; Kamred Udham Singh; Ankit Kumar; Saroj Kumar Pandey; Neeraj varshney; Teekam Singh; Chetan Swarup; Kamred Udham Singh; Ankit Kumar; Saroj Kumar Pandey; Neeraj varshney; Teekam Singh

doi:10.3934/era.2023146

Electronic Research Archive

2023, Volume 31, Issue 5: 2900-2924. doi: 10.3934/era.2023146

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Research article Special Issues

Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches

1.
Department of Basic Science, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh-Male Campus, Riyadh 11673, Saudi Arabia
2.
School of Computing, Graphic Hill Era University, Dehradun-248002, India
3.
Department of Computer Engineering & Applications, GLA University, Mathura, India
4.
Department of Mathematics, University of Petroleum & Energy Studies, Dehradun-248002, India

Received: 30 November 2022 Revised: 03 March 2023 Accepted: 06 March 2023 Published: 16 March 2023

The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis.
- image segmentation,
- pattern classification,
- MRI images,
- tumor,
- PCA,
- SVM
Citation: Chetan Swarup, Kamred Udham Singh, Ankit Kumar, Saroj Kumar Pandey, Neeraj varshney, Teekam Singh. Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches[J]. Electronic Research Archive, 2023, 31(5): 2900-2924. doi: 10.3934/era.2023146

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Abstract

The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis.

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