Automated machine learning-based neural network for brain tumor classification

Research Article
Open access

Automated machine learning-based neural network for brain tumor classification

Shim Ho 1*
  • 1 Hong Kong University of Science and Technology    
  • *corresponding author hshimaa@connect.ust.hk
Published on 4 February 2024 | https://doi.org/10.54254/2755-2721/35/20230401
ACE Vol.35
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-295-4
ISBN (Online): 978-1-83558-296-1

Abstract

In the contemporary medical landscape, characterized by the widespread and pernicious affliction of brain tumors, the imperative to enhance diagnostic modalities is paramount, aligning with the overarching objective of precisely delineating the affected patient cohort, thereby affording the prospect of administering expeditious therapeutic interventions. Within this scholarly discourse, the principal objective pertains to the discernment of an optimal Convolutional Neural Network (CNN) model, engendered through the mechanizations of automated machine learning, as instantiated within the virtual precincts of the "Edge Impulse" online platform. The corpus of investigation entails the acquisition of requisite data sets from the digital repository denominated "Kaggle," specialized in the domain of scientific knowledge. The amassed data sets, having undergone meticulous preprocessing procedures, were subsequently subjected to partitioning activities within the confines of the "Edge Impulse" framework, wherein a standardized ratio of division, namely a four-to-one proportionality between training and testing subsets, was consistently maintained across discrete data clusters. The training and testing processes were accomplished on Edge Impulse. The image mode, data learning block, learning rate, et cetera, were modified for each neural network models trained on Edge Impulse. Each model performed differently, and the distinct testing accuracy and on device performances were collected for each model for comparison. The experimental results demonstrate that using transfer learning supported by Edge Impulse with learning rate equals to 0.00051 and “fit longest axis” image resize mode is the optimal option for training a brain classifying model on Edge Impulse through automated machine learning.

Keywords:

Convolutional Neural Network, artificial intelligence, brain tumor, classification

Ho,S. (2024). Automated machine learning-based neural network for brain tumor classification. Applied and Computational Engineering,35,245-252.
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References

[1]. American Brain Tumor Association (n.d.) About Brain Tumors Availabe at: https://www.abta.org/about-brain-tumors/ (Accessed: 12 August 2023)

[2]. National Brain Tumor Society 2023 Brain tumor facts Available at: https://braintumor.org/brain-tumors/about-brain-tumors/brain-tumor-facts/#:~:text=35.7%25%20Relative%20Survival%20Rate%20for%20all%20patients%20with,die%20from%20a%20malignant%20brain%20tumor%20in%202023 (Accessed: 12 August 2023)

[3]. Lupton A Abu-Suwa H Bolton G C and Golden C 2020 The implications of brain tumors on aggressive behavior and suicidality: a review Aggression and violent behavior 54 p 101416

[4]. Qiu Y Chang C S Yan J L et al 2019 Semantic segmentation of intracranial hemorrhages in head CT scans 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) IEEE 112-115

[5]. Ayadi W Elhamzi W Charfi I et al 2021 Deep CNN for brain tumor classification Neural processing letters 53: 671-700

[6]. Gull S and Akbar S 2021 Artificial intelligence in brain tumor detection through MRI Scans Artif Intelligence Internet Things pp 241-276

[7]. Dahhaghchi I Christie R D Rosenwald G W and Liu C C 1997 AI application areas in power systems IEEE expert 12(1) pp 58-66

[8]. Ahmad T Zhu H Zhang D Tariq R Bassam A Ullah F AlGhamdi A S and Alshamrani S S 2022 Energetics Systems and artificial intelligence: Applications of industry 4.0 Energy Reports 8 pp 334-361

[9]. Yan Z Liu W Wen S and Yang Y 2019 Multi-label image classification by feature attention network Ieee Access 7 pp 98005-98013

[10]. Albahri O S et al 2020 Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects Journal of infection and public health 13(10) pp 1381-1396

[11]. Hekler A et al 2019 Superior skin cancer classification by the combination of human and artificial intelligence European Journal of Cancer 120 pp 114-121

[12]. Géron A 2022 Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow O'Reilly Media, Inc. chapter 2 pp 66-91

Cite this article

Ho,S. (2024). Automated machine learning-based neural network for brain tumor classification. Applied and Computational Engineering,35,245-252.

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 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-295-4(Print) / 978-1-83558-296-1(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.35
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
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References

[1]. American Brain Tumor Association (n.d.) About Brain Tumors Availabe at: https://www.abta.org/about-brain-tumors/ (Accessed: 12 August 2023)

[2]. National Brain Tumor Society 2023 Brain tumor facts Available at: https://braintumor.org/brain-tumors/about-brain-tumors/brain-tumor-facts/#:~:text=35.7%25%20Relative%20Survival%20Rate%20for%20all%20patients%20with,die%20from%20a%20malignant%20brain%20tumor%20in%202023 (Accessed: 12 August 2023)

[3]. Lupton A Abu-Suwa H Bolton G C and Golden C 2020 The implications of brain tumors on aggressive behavior and suicidality: a review Aggression and violent behavior 54 p 101416

[4]. Qiu Y Chang C S Yan J L et al 2019 Semantic segmentation of intracranial hemorrhages in head CT scans 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) IEEE 112-115

[5]. Ayadi W Elhamzi W Charfi I et al 2021 Deep CNN for brain tumor classification Neural processing letters 53: 671-700

[6]. Gull S and Akbar S 2021 Artificial intelligence in brain tumor detection through MRI Scans Artif Intelligence Internet Things pp 241-276

[7]. Dahhaghchi I Christie R D Rosenwald G W and Liu C C 1997 AI application areas in power systems IEEE expert 12(1) pp 58-66

[8]. Ahmad T Zhu H Zhang D Tariq R Bassam A Ullah F AlGhamdi A S and Alshamrani S S 2022 Energetics Systems and artificial intelligence: Applications of industry 4.0 Energy Reports 8 pp 334-361

[9]. Yan Z Liu W Wen S and Yang Y 2019 Multi-label image classification by feature attention network Ieee Access 7 pp 98005-98013

[10]. Albahri O S et al 2020 Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects Journal of infection and public health 13(10) pp 1381-1396

[11]. Hekler A et al 2019 Superior skin cancer classification by the combination of human and artificial intelligence European Journal of Cancer 120 pp 114-121

[12]. Géron A 2022 Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow O'Reilly Media, Inc. chapter 2 pp 66-91

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