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Performance Analysis of Pre-Trained Convolutional Models for Brain Tumor Classification

Performance Analysis of Pre-Trained Convolutional Models for Brain Tumor Classification

Rishabh Chauhan, Garima Aggarwal
Copyright: © 2023 |Pages: 24
ISBN13: 9781668469804|ISBN10: 1668469804|ISBN13 Softcover: 9781668469811|EISBN13: 9781668469828
DOI: 10.4018/978-1-6684-6980-4.ch010
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MLA

Chauhan, Rishabh, and Garima Aggarwal. "Performance Analysis of Pre-Trained Convolutional Models for Brain Tumor Classification." Exploring Future Opportunities of Brain-Inspired Artificial Intelligence, edited by Madhulika Bhatia, et al., IGI Global, 2023, pp. 157-180. https://doi.org/10.4018/978-1-6684-6980-4.ch010

APA

Chauhan, R. & Aggarwal, G. (2023). Performance Analysis of Pre-Trained Convolutional Models for Brain Tumor Classification. In M. Bhatia, T. Choudhury, & B. Dewangan (Eds.), Exploring Future Opportunities of Brain-Inspired Artificial Intelligence (pp. 157-180). IGI Global. https://doi.org/10.4018/978-1-6684-6980-4.ch010

Chicago

Chauhan, Rishabh, and Garima Aggarwal. "Performance Analysis of Pre-Trained Convolutional Models for Brain Tumor Classification." In Exploring Future Opportunities of Brain-Inspired Artificial Intelligence, edited by Madhulika Bhatia, Tanupriya Choudhury, and Bhupesh Kumar Dewangan, 157-180. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-6980-4.ch010

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Abstract

Brain tumor is a common tumor and is damaging depending upon the type of tumor and the stage at which it is diagnosed. It is revealed by a doctor using magnetic resonance imaging of the brain. Analyzing these images is an exacting task, and human intervention might be a scope of error. Therefore, applying deep learning-based image classification systems can play a crucial role in classifying several tumors. This chapter aims to implement, analyze, and compare pre-trained convolutional neural network models and a proposed neural architecture to classify brain tumors. The dataset includes 7000 images classified into four classes of tumors: glioma, meningioma, no tumor, and pituitary. The proposed methodology involves cautious analysis of data and the development of a deep learning model. This has produced testing results with high accuracy of 99.0% and an error rate of 6.8%. According to the experimental findings, the proposed method for classifying brain tumors has a respectable level of accuracy and a low error rate, making it an appropriate tool for use in real-time applications.

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