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

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Enhanced Brain Tumor Diagnosis Using Transfer Learning Medical Imaging

Authors
Gugulothu Venkanna1, *, Ganda Sravani1, Devireddy Nandini1, Punjala Reethu1
1Department of CSE, Sreenidhi Institute of Science and Technology, Ghatkesar, Hyderabad, India
*Corresponding author. Email: venkanna.g@sreenidhi.edu.in
Corresponding Author
Gugulothu Venkanna
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_140How to use a DOI?
Keywords
Brain Tumor Classification; ResNet-50; Transfer Learning; MRI Imaging; Deep Learning
Abstract

Early detection and treatment planning are greatly aided by the use of MRI scans for brain tumor diagnosis. Convolutional Neural Networks (CNNs) and traditional machine learning frequently suffer from feature extraction constraints, which results in less than ideal classification performance. Using transfer learning with ResNet50, a deep learning-based model that has been pre-trained on extensive image datasets, we present an improved brain tumor diagnosis system in this study. Using data augmentation and class balancing techniques to improve generalization, the model is refined to classify MRI images into Tumor and No Tumor categories. According to our experimental findings, ResNet50 performs noticeably better than traditional CNN models, obtaining greater accuracy, resilience, and fewer misclassification rates. The suggested technique helps radiologists make a accurate and timely diagnosis by providing an automated, scalable, and effective method for brain tumor detection in medical imaging.

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_140How 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  - Gugulothu Venkanna
AU  - Ganda Sravani
AU  - Devireddy Nandini
AU  - Punjala Reethu
PY  - 2025
DA  - 2025/11/04
TI  - Enhanced Brain Tumor Diagnosis Using Transfer Learning Medical Imaging
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1715
EP  - 1724
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_140
DO  - 10.2991/978-94-6463-858-5_140
ID  - Venkanna2025
ER  -