Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Mammogram Image Deblurring, Pectoral Removal, and Tumor Visualization for Radiologists Patients with AI-Powered Assistance

Authors
Shadulla Shaikh1, *, Mavia Ansari2, Toha Burondkar2, Manasi Shimpi2, Salim Shaikh2
1Department of Computer Engineering, Anjuman-I-Islam’s Kalsekar Technical Campus, Navi-Mumbai, New Panvel, 410206, Maharashtra, India
2Department of Computer Engineering, Anjuman-I-Islam’s Kalsekar Technical Campus, Navi-Mumbai, New Panvel, 410206, Maharashtra, India
*Corresponding author. Email: sshadulla22@gmail.com
Corresponding Author
Shadulla Shaikh
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_10How to use a DOI?
Keywords
Breast cancer; mammogram enhancement; pectoral muscle removal; AI in radiology; 3D tumor visualization; breast cancer diagnosis
Abstract

Breast cancer remains a leading cause of mortality among women worldwide, with early detection being critical for improving survival rates. However, mammogram interpretation is often hindered by image blurring, pectoral muscle interference, and limited 2D visualization. This paper presents MammoCare, an AI-powered platform that addresses these challenges through: (1) advanced image deblurring techniques, (2) automated and manual pectoral muscle removal, and (3) enhanced 3D/4D tumor visualization. The system integrates DeepSeek AI for real-time diagnostic assistance and a GPT-3.5-powered chatbot for patient education. By improving image clarity and diagnostic accuracy, MammoCare aims to reduce interpretation errors and support early breast cancer detection in clinical and remote settings.

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 the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_10How 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  - Shadulla Shaikh
AU  - Mavia Ansari
AU  - Toha Burondkar
AU  - Manasi Shimpi
AU  - Salim Shaikh
PY  - 2025
DA  - 2025/10/07
TI  - Mammogram Image Deblurring, Pectoral Removal, and Tumor Visualization for Radiologists Patients with AI-Powered Assistance
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 149
EP  - 161
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_10
DO  - 10.2991/978-94-6463-852-3_10
ID  - Shaikh2025
ER  -