Brain tumor classification using CNN: Difference between optimizers

Research Article
Open access

Brain tumor classification using CNN: Difference between optimizers

Yizhou Li 1*
  • 1 Shanghai Starriver Bilingual School, Shanghai, 201108, China    
  • *corresponding author 15010340135@xs.hnit.edu.cn
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

The classification of brain tumor using artificial intelligence has long been a heated topic and never failed to arouse public attention, being both efficient and useful. Each year, around nearly 12000 people are diagnosed with brain tumor, and the AI approach allows a number of specific cases to be identified quickly with few errors, contributing in saving millions of lives. This paper uses CNN (ResNet50 architecture) for classification and evaluates the performances of three kinds of optimizers – adaptive moment estimation (Adam), stochastic gradient descent (SGD), and genetic algorithm (GA) – when being applied to the model. The resulting accuracy scores are, respectively, 93%, 90%, and 95%, which demonstrates that genetic algorithm performs the best, suggesting a fine choice of utilization in practical scenarios. The results as well show the most precise diagnosis on pituitary tumor and the least on meningioma tumor, providing a direction for future improvement on dataset and training parameters.

Keywords:

convolutional neural network, brain tumor classification, optimizers, comparison.

Li,Y. (2023). Brain tumor classification using CNN: Difference between optimizers. Applied and Computational Engineering,5,704-711.
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References

[1]. Rajkomar A, Dean J and Kohane I 2019 Machine learning in medicine N Engl J MedNew 380 1347-58.

[2]. Fujita, H 2020 AI-based computer-aided diagnosis (AI-CAD): The Latest Review to read first Radiological Physics and Technology 13 6-19.

[3]. LeCun Y, Bengio Y and Hinton G 2015 Deep learning Nature 521 pp 436-44.

[4]. Ting D S, Liu Y, Burlina P, Xu X, Bressler N M and Wong T Y 2018 AI for Medical Imaging Goes Deep Nature Medicine 24 pp 539-40.

[5]. Wang B, Jin S, Yan Q, Xu H, Luo C, Wei L, Zhao W, et.al. 2021 AI-assisted CT Imaging Analysis for COVID-19 screening: Building and deploying a medical AI system Applied Soft Computing 98 106897.

[6]. Mayo Foundation for Medical Education and Research 2022 Meningioma Mayo Clinic.

[7]. Mayo Foundation for Medical Education and Research 2020 Glioma Mayo Clinic.

[8]. Mayo Foundation for Medical Education and Research 2021 Pituitary Tumors Mayo Clinic.

[9]. Yamashita R, Nishio M, Do R K and Togashi K 2018 Convolutional Neural Networks: An overview and application in Radiology Insights into Imaging 9 pp 611-29.

[10]. Weile D S and Michielssen E 1997 Genetic algorithm optimization applied to electromagnetics: a review IEEE Transactions on Antennas and Propagation 45 pp 343–53.

[11]. Nyúl L G, Udupa J K and Zhang X 2000 New variants of a method of MRI scale standardization IEEE Transactions on Medical Imaging 19 pp 143–50.

Cite this article

Li,Y. (2023). Brain tumor classification using CNN: Difference between optimizers. Applied and Computational Engineering,5,704-711.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Rajkomar A, Dean J and Kohane I 2019 Machine learning in medicine N Engl J MedNew 380 1347-58.

[2]. Fujita, H 2020 AI-based computer-aided diagnosis (AI-CAD): The Latest Review to read first Radiological Physics and Technology 13 6-19.

[3]. LeCun Y, Bengio Y and Hinton G 2015 Deep learning Nature 521 pp 436-44.

[4]. Ting D S, Liu Y, Burlina P, Xu X, Bressler N M and Wong T Y 2018 AI for Medical Imaging Goes Deep Nature Medicine 24 pp 539-40.

[5]. Wang B, Jin S, Yan Q, Xu H, Luo C, Wei L, Zhao W, et.al. 2021 AI-assisted CT Imaging Analysis for COVID-19 screening: Building and deploying a medical AI system Applied Soft Computing 98 106897.

[6]. Mayo Foundation for Medical Education and Research 2022 Meningioma Mayo Clinic.

[7]. Mayo Foundation for Medical Education and Research 2020 Glioma Mayo Clinic.

[8]. Mayo Foundation for Medical Education and Research 2021 Pituitary Tumors Mayo Clinic.

[9]. Yamashita R, Nishio M, Do R K and Togashi K 2018 Convolutional Neural Networks: An overview and application in Radiology Insights into Imaging 9 pp 611-29.

[10]. Weile D S and Michielssen E 1997 Genetic algorithm optimization applied to electromagnetics: a review IEEE Transactions on Antennas and Propagation 45 pp 343–53.

[11]. Nyúl L G, Udupa J K and Zhang X 2000 New variants of a method of MRI scale standardization IEEE Transactions on Medical Imaging 19 pp 143–50.

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