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

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Deep-CNN Based Brain Tumor Classification from MRI Images

Authors
Bhukya Shankar1, *, Krishna Dharavath2, K. V. Sridhar3, E. Chandra Sekhar4, P. Chandra Sekhar4, G. Ravi Kumar4
1ECE Dept, CVR College of Engineering, Ibrahimpatnam, Hyderabad, India
2ECE Dept, Vardhaman College of Engineering, Shamshabad, Hyderabad, India
3Department of ECE, NIT Warangal, Hanamkonda, Telangana, India
4ECE Dept, Chaitanya Bharathi Institute of Technology (A), Hyderabad, India
*Corresponding author. Email: shankar.engg1577@gmail.com
Corresponding Author
Bhukya Shankar
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_96How to use a DOI?
Keywords
Additive manufacturing; Sustainable practices; computer-aided design (CAD) model; Waste reduction; Manufacturing Eco system
Abstract

Brain tumors are deadly and early diagnosis is crucial for successful treatment. Traditionally, radiologists rely on their experience to analyze brain scans, but this can be time-consuming and error-prone, especially with a growing number of patients. This paper presents a fast and efficient deep learning-based method for brain tumor detection in MRI images, aimed at enhancing medical decision-making. We propose a custom deep Convolutional Neural Network (C-CNN) model, developed alongside four pre-trained models: VGG16, ResNet50, MobileNet, and InceptionV3. The models were trained and tested on a comprehensive dataset of brain tumor images. The custom C-CNN model demonstrated superior performance, achieving 100% training accuracy, 99.11% validation accuracy, and 99.27% testing accuracy. These results highlight the model’s reliability and potential for accurate, rapid identification of brain tumors, offering a valuable tool for improving patientcare.

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_96How 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  - Bhukya Shankar
AU  - Krishna Dharavath
AU  - K. V. Sridhar
AU  - E. Chandra Sekhar
AU  - P. Chandra Sekhar
AU  - G. Ravi Kumar
PY  - 2025
DA  - 2025/11/04
TI  - Deep-CNN Based Brain Tumor Classification from MRI Images
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1152
EP  - 1165
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_96
DO  - 10.2991/978-94-6463-858-5_96
ID  - Shankar2025
ER  -