Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Automated Brain Tumor Recognition Using Deep Learning And MRI Analysis

Authors
T. Boopathy1, *, Bharani Dharan1, Gottapu Lakshmana  Rao1, K. C. Krishnakanth1, S Gokulraj1
1Dhanalakshmi Srinivasan Engineering College, Preambalur, Tamilnadu, India
*Corresponding author. Email: boopathy.t@dsengg.ac.in
Corresponding Author
T. Boopathy
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_206How to use a DOI?
Keywords
Brain Tumor; Deep Learning; MRI; U-Net; ResNet-50; Medical Imaging.First Keyword; Second Keyword; Third Keyword
Abstract

Brain tumor recognition is a serious task in medical imaging, helping in main diagnosis and handling development. This paper shows a deep learning-based method utilizing MRI scans for tumor detection and classification. The model integrates U-Net intended for splitting up and ResNet-50 for classification, achieving high accuracy in distinguishing between tumor types. Data preprocessing, including normalization and augmentation, enhances model robustness. The suggested technique outperforms conventional machine learning paradigms such during SVM and Random Forest, with a recorded accuracy of 96.4%. Clinical validation ensures real-world applicability, making the system suitable for implementation in healthcare sets. The study emphasizes continuous model improvement through retraining and hyperparameter tuning. This automated system aims to assist radiologists by reducing diagnostic time and improving accuracy. Future research will explore multi-modal data integration and explainability techniques to enhance interpretability for medical professionals.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_206How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - T. Boopathy
AU  - Bharani Dharan
AU  - Gottapu Lakshmana  Rao
AU  - K. C. Krishnakanth
AU  - S  Gokulraj
PY  - 2025
DA  - 2025/11/04
TI  - Automated Brain Tumor Recognition Using Deep Learning And MRI Analysis
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2465
EP  - 2477
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_206
DO  - 10.2991/978-94-6463-858-5_206
ID  - Boopathy2025
ER  -