AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025390079
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ORIGINAL RESEARCH ARTICLE

A comprehensive system for breast cancer screening using rotational thermography and dynamic thermal infrared imaging

Asok Bandyopadhyay1* ,  Bivas Dam2 ,  Dipak Chandra Patranabis2
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1 Department of ICT and Services, Centre for Development of Advanced Computing, Kolkata, West Bengal, India
2 Department of Instrumentation and Electronics Engineering, Faculty of Engineering and Technology, Jadavpur University, Kolkata, West Bengal, India
AIH, 025390079 https://doi.org/10.36922/AIH025390079
Received: 25 September 2025 | Revised: 3 November 2025 | Accepted: 11 November 2025 | Published online: 25 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Integrated rotational thermography and dynamic infrared imaging enable accurate, non-invasive, and early breast cancer (BC) detection. This study presents an integrated framework for a novel BC screening approach that combines dynamic thermal imaging and comprehensive angle coverage. The proposed system aims to achieve high accuracy and a reliable method for BC detection by addressing the limitations of conventional infrared imaging approaches. By integrating innovative camera technologies, rotational thermography, and advanced data analysis techniques, the system overcomes challenges, such as incomplete breast tissue coverage, limited adaptability to diverse patient profiles, and limitations in data analysis and diagnostic yield. The research methodology comprised pilot studies (PS1, PS2, and PS3), followed by a final study, utilizing various data collection techniques and analysis methods. Infrared images were captured using various infrared cameras, including both low-resolution and high-resolution models. Rotational thermography provided multiple-angle imaging, improving abnormality detectability. User-interface-based image processing software and machine learning algorithms were employed for efficient data analysis and feature extraction. The results demonstrated accurate binary classification of normal versus abnormal breast tissue, achieving sensitivity, specificity, and accuracy of 86.67%, 97.30%, and 93.27%, respectively. In addition, this study highlights the importance of considering angiogenesis in BC screening, as infrared imaging can detect hypervascularity and hyperthermia in non-palpable BCs. Integrating angiogenesis-related factors with infrared imaging can facilitate early detection and enhance prognostic outcomes. Overall, the proposed BC screening system overcomes limitations through dynamic temperature-based imaging, comprehensive angle coverage, and consideration of angiogenesis.

Graphical abstract
Keywords
Breast cancer screening
Dynamic temperature-based imaging infrared imaging
Rotational thermography
Angiogenesis
Biomedical engineering
Machine learning
Data analysis techniques
Early detection
Funding
This project was funded by the Ministry of Electronics and Information Technology, Government of India (No. 1[4/2015).
Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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