A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms

Document Type : Original Paper

Authors

1 Department of Biomedical Engineering, Iranian Research Organization for Science & Technology (IROST), Tehran, Iran

2 Biomaterials and Tissue Engineering Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran

3 Breast Diseases Department, Motamed Cancer Institute, South Gandhi St., Tehran, IR Iran.

4 Iranian Centre for Breast Cancer (ICBC), ACECR, Tehran, Iran

5 Genetics Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, South Gandhi, Vanak Square, Tehran 1517964311, Iran.

6 Department of Computer Engineering, Faculty of Engineering, Islamic Azad University E-Campus, Tehran, Iran

7 Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

8 Electrical Engineering Department, Iranian Research Organization for Science and Technology, Tehran, Iran

Abstract

Introduction: Several types of cancer can be detected early through thermography, which uses thermal profiles to image tissues in recent years, thermography has gained increasing attention due to its non-invasive and radiation-free nature. There is a growing need for thermographic images of breast cancer lesions in different nationalities and ages to develop this technique, however. This study aims to introduce a dataset of breast thermograms.
Material and Methods: In this study, thermographic images of breast cancer from Iranian samples were prepared and confirmed due to the limited number of breast thermogram databases.  The prepared database was tested using artificial intelligence and another well-known DMR database (Database for Mastology Research) in this study to determine its reliability.
Results: A variety of deep learning architectures and transfer learning are used to evaluate these databases for accuracy, sensitivity, speed, training compliance, and validation compliance. According to best-fitted structures for both types of databases, the database obtained from this study has a quality comparable to the DMR reference database, with minimum accuracy, sensitivity, specificity, precision, and F-score of 80%, 86%, 86%, 88%, and 87%, respectively.
Conclusion: Using thermography as a method of early breast screening is demonstrated to be effective. In comparison to DMR, the lower statistics of the proposed database (between 2 and 7 percent) indicates that more diverse breast thermograms should be captured in conjunction with improvements to imaging equipment as well as adherence to thermography recording protocols in order to improve the reliability and efficiency of the database.

Keywords

Main Subjects

References

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Iranian Journal of Medical Physics
Volume 21, Issue 4 - Serial Number 4
July and August 2024
Pages 237-248

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History

  • Receive Date: 10 April 2023
  • Revise Date: 19 June 2023
  • Accept Date: 02 July 2023

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