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

<Previous Article In Volume
Next Article In Volume>

Detection and Risk Prediction of Brain Tumor using Model-Agnostic Explainable Artificial Intelligence

Authors
Ramesh Alladi1, *, R. N. V. Jagan Mohan2, K. V. Ramana3, P. Sumithabhashini4
1Associate Professor, CSE, ACE Engineering College, Hyderabad, TS, India
2Professor, CSE, SRKR Engineering College, Bhimavaram, AP, India
3Professor of CSE and Rector JNTU Kakinada, AP, Hyderabad, India
4Professor, ECE, Holy Mary Institute of Technology and Science, Hyderabad, TS, India
*Corresponding author.
Corresponding Author
Ramesh Alladi
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_235How to use a DOI?
Keywords
Detection; Brain tumor; XAI Frame works
Abstract

Most people are suffering from brain tumors in recent days. In medical image processing, brain tumor detection and risk prediction continue to be important problems needing very accurate models for early diagnosis and treatment planning. Using model-agnostic explainable Artificial Intelligence (XAI), this study offers a novel solution for identifying and forecasting brain tumor risk. By combining cutting-edge deep learning methods with XAI frameworks, our approach attains great accuracy in tumor detection and offers understandable insights on the model’s decision-making process. By providing visual and statistical justifications for each prediction, this explainability helps doctors to trust automated systems. Considering variables including tumor kind, size, and location, our method employs various imaging techniques to identify and forecast brain tumors. Furthermore, enhancing personalized treatment plans is the model’s risk prediction capability, which evaluates tumor development probability. Our tumor databases highlight better performance than conventional models, therefore underlining their efficacy. This research shows how XAI might raise brain tumor detection’s transparency and accuracy, therefore improving patient outcomes and clinical judgment.

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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
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_235How 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  - Ramesh Alladi
AU  - R. N. V. Jagan Mohan
AU  - K. V. Ramana
AU  - P. Sumithabhashini
PY  - 2025
DA  - 2025/11/04
TI  - Detection and Risk Prediction of Brain Tumor using Model-Agnostic Explainable Artificial Intelligence
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2808
EP  - 2819
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_235
DO  - 10.2991/978-94-6463-858-5_235
ID  - Alladi2025
ER  -