A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms
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 c...
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| Format: | Article |
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Mashhad University of Medical Sciences
2024-07-01
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| Series: | Iranian Journal of Medical Physics |
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| Online Access: | https://ijmp.mums.ac.ir/article_22650_9f0878b87578de465a02e476604885cb.pdf |
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| author | Mohamad Firouzmand Keivan Majidzadeh Maryam Jafari Shahpar Haghighat Rezvan Esmaeili Leila Moradi Nima Misaghi Mahsa Ensafi Fatemeh Batmanghelich Mohammadreza Keyvanpour Seyed Vahab Shojaedini |
| author_facet | Mohamad Firouzmand Keivan Majidzadeh Maryam Jafari Shahpar Haghighat Rezvan Esmaeili Leila Moradi Nima Misaghi Mahsa Ensafi Fatemeh Batmanghelich Mohammadreza Keyvanpour Seyed Vahab Shojaedini |
| author_sort | Mohamad Firouzmand |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9089783ef7044bc489ade5f62c988b48 |
| institution | Kabale University |
| issn | 2345-3672 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Mashhad University of Medical Sciences |
| record_format | Article |
| series | Iranian Journal of Medical Physics |
| spelling | doaj-art-9089783ef7044bc489ade5f62c988b482025-01-18T07:32:32ZengMashhad University of Medical SciencesIranian Journal of Medical Physics2345-36722024-07-0121423724810.22038/ijmp.2023.71683.226822650A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast ThermogramsMohamad Firouzmand0Keivan Majidzadeh1Maryam Jafari2Shahpar Haghighat3Rezvan Esmaeili4Leila Moradi5Nima Misaghi6Mahsa Ensafi7Fatemeh Batmanghelich8Mohammadreza Keyvanpour9Seyed Vahab Shojaedini10Department of Biomedical Engineering, Iranian Research Organization for Science & Technology (IROST), Tehran, IranBiomaterials and Tissue Engineering Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, IranBreast Diseases Department, Motamed Cancer Institute, South Gandhi St., Tehran, IR Iran.Iranian Centre for Breast Cancer (ICBC), ACECR, Tehran, IranGenetics Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, South Gandhi, Vanak Square, Tehran 1517964311, Iran.Department of Biomedical Engineering, Iranian Research Organization for Science & Technology (IROST), Tehran, IranDepartment of Computer Engineering, Faculty of Engineering, Islamic Azad University E-Campus, Tehran, IranDepartment of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, IranDepartment of Computer Engineering, Faculty of Engineering, Islamic Azad University E-Campus, Tehran, IranDepartment of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, IranElectrical Engineering Department, Iranian Research Organization for Science and Technology, Tehran, IranIntroduction: 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.https://ijmp.mums.ac.ir/article_22650_9f0878b87578de465a02e476604885cb.pdfbreast cancermedical imagingartificial intelligencedeep learningtemperature mapping |
| spellingShingle | Mohamad Firouzmand Keivan Majidzadeh Maryam Jafari Shahpar Haghighat Rezvan Esmaeili Leila Moradi Nima Misaghi Mahsa Ensafi Fatemeh Batmanghelich Mohammadreza Keyvanpour Seyed Vahab Shojaedini A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms Iranian Journal of Medical Physics breast cancer medical imaging artificial intelligence deep learning temperature mapping |
| title | A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms |
| title_full | A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms |
| title_fullStr | A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms |
| title_full_unstemmed | A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms |
| title_short | A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms |
| title_sort | framework for promoting passive breast cancer monitoring deep learning as an interpretation tool for breast thermograms |
| topic | breast cancer medical imaging artificial intelligence deep learning temperature mapping |
| url | https://ijmp.mums.ac.ir/article_22650_9f0878b87578de465a02e476604885cb.pdf |
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