Enhanced brain tumor detection from brain MRI images using convolutional neural networks

Abhimanu Singh; Smita Jain; Abhimanu Singh; Smita Jain

doi:10.3934/bioeng.2025010

AIMS Bioengineering

2025, Volume 12, Issue 2: 215-224. doi: 10.3934/bioeng.2025010

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Research article

Enhanced brain tumor detection from brain MRI images using convolutional neural networks

Abhimanu Singh ^{1,2
,
,},
Smita Jain ²

1.
Department of Applied Mathematics, Bhagwan Parshuram Institute of Technology, Rohini, Delhi, India
2.
Department of Mathematics, JECRC University, Jaipur, Rajasthan, India

Received: 18 March 2025 Revised: 23 April 2025 Accepted: 28 April 2025 Published: 28 May 2025

The brain is one of the most important organs of a human body. It controls all activities of the body and hence is referred to as the CPU of human body. Fast, accurate and early diagnosis of brain tumors is essential for better treatment plans that potentially result in larger survival rates. Automatic and non-invasive brain tumor detection methods are highly relevant. Continuous efforts by researchers have resulted in a significant increase in brain tumor detection accuracy, but 100% accuracy on testing/validation data is still challenging to obtain. This paper presents a convolutional neural network model that significantly enhances brain tumor classification accuracy with low complexity. To do so, Kaggle's publicly available dataset titled “Brain_Tumor_Detection_MRI”, consisting of 2891 brain MRI images annotated as “yes” (having tumors) or “no” (without tumors), was used. By leveraging advanced deep learning techniques, we achieved an accuracy of 99.31% on the test dataset. This value shows a significant improvement in classification accuracy, showcasing the potential of convolutional neural networks in medical imaging and diagnostic applications.
- Deep Learning,
- convolutional neural network,
- model,
- brain tumor detection,
- accuracy
Citation: Abhimanu Singh, Smita Jain. Enhanced brain tumor detection from brain MRI images using convolutional neural networks[J]. AIMS Bioengineering, 2025, 12(2): 215-224. doi: 10.3934/bioeng.2025010

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Abstract

The brain is one of the most important organs of a human body. It controls all activities of the body and hence is referred to as the CPU of human body. Fast, accurate and early diagnosis of brain tumors is essential for better treatment plans that potentially result in larger survival rates. Automatic and non-invasive brain tumor detection methods are highly relevant. Continuous efforts by researchers have resulted in a significant increase in brain tumor detection accuracy, but 100% accuracy on testing/validation data is still challenging to obtain. This paper presents a convolutional neural network model that significantly enhances brain tumor classification accuracy with low complexity. To do so, Kaggle's publicly available dataset titled “Brain_Tumor_Detection_MRI”, consisting of 2891 brain MRI images annotated as “yes” (having tumors) or “no” (without tumors), was used. By leveraging advanced deep learning techniques, we achieved an accuracy of 99.31% on the test dataset. This value shows a significant improvement in classification accuracy, showcasing the potential of convolutional neural networks in medical imaging and diagnostic applications.

Acknowledgments

The authors are thankful to the management and administration of their parent institutions for providing the facility required to conduct this research. Also, the authors are thankful to Shivam Singh, student of B. Tech, (IT) at Bhagwan Parshuram Institute of Technology, Rohini, Delhi, India, for his cooperation in execution of the code. Special thank goes to Google Colab for providing the computing facility, without which it could have not been possible.

Conflict of interest

Authors declare that they have no conflict of interest.

Author contributions:

Abhimanu Singh: Concept, drafting, design, data acquisition, editing, experimenting. Analysis.
Smita Jain: concept, design, manuscript reviewing, analysis.

References

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