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

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Deep Learning-Driven Medical Image Captioning with eXplainable AI

Authors
Vineet Raj Singh Kushwah1, *, Ashok Shrivastava2
1Research Scholar, Department of CSE, ASET, Amity University, Gwalior, Madhya Pradesh, India
2Associate Professor, Department of CSE, ASET, Amity University, Gwalior, Madhya Pradesh, India
*Corresponding author. Email: vineetkushwah@yahoo.co.in
Corresponding Author
Vineet Raj Singh Kushwah
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_8How to use a DOI?
Keywords
Artificial Intelligence; Deep Learning; Medical Imaging; eXplainable AI (XAI)
Abstract

Medical Image Captioning, which is the automatic generation of descriptive text through medical images, has yet to be a very valuable assistant for clinicians regarding image interpretation and diagnosis. Notable in particular is the recent break-through on deep learning that has greatly improved the accuracy and efficiency of image captioning model. However, on the other hand this “black-box” nature of deep learning model raises even more worrisome questions about interpretability and trustworthiness in a crucial field like healthcare. To address these challenges, incorporation of eXplainable AI (XAI) techniques in the framework of medical image captioning will enhance the transparency of these model decisions. This paper investigates about various XAI techniques such as GRAD-CAM, LIME and other attention mechanism to provide visual explanation of model output. The paper covers the challenges and further possibilities in order to enhance the trustworthiness and interpretability at the same time.

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_8How 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  - Vineet Raj Singh Kushwah
AU  - Ashok Shrivastava
PY  - 2025
DA  - 2025/11/04
TI  - Deep Learning-Driven Medical Image Captioning with eXplainable AI
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 75
EP  - 87
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_8
DO  - 10.2991/978-94-6463-858-5_8
ID  - Kushwah2025
ER  -