سامانه مدیریت نشریات علمی دانشگاه علوم پزشکی مشهد

Firouzmand, Mohamad, Majidzadeh, Keivan, Jafari, Maryam, Haghighat, Shahpar, Esmaeili, Rezvan, Moradi, Leila, Misaghi, Nima, Ensafi, Mahsa, Batmanghelich, Fatemeh, Keyvanpour, Mohammadreza, Shojaedini, Seyed Vahab. (1403). A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms. , 21(4), 237-248. doi: 10.22038/ijmp.2023.71683.2268
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". , 21, 4, 1403, 237-248. doi: 10.22038/ijmp.2023.71683.2268
Firouzmand, Mohamad, Majidzadeh, Keivan, Jafari, Maryam, Haghighat, Shahpar, Esmaeili, Rezvan, Moradi, Leila, Misaghi, Nima, Ensafi, Mahsa, Batmanghelich, Fatemeh, Keyvanpour, Mohammadreza, Shojaedini, Seyed Vahab. (1403). 'A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms', , 21(4), pp. 237-248. doi: 10.22038/ijmp.2023.71683.2268
Firouzmand, Mohamad, Majidzadeh, Keivan, Jafari, Maryam, Haghighat, Shahpar, Esmaeili, Rezvan, Moradi, Leila, Misaghi, Nima, Ensafi, Mahsa, Batmanghelich, Fatemeh, Keyvanpour, Mohammadreza, Shojaedini, Seyed Vahab. A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms. , 1403; 21(4): 237-248. doi: 10.22038/ijmp.2023.71683.2268

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

Iranian Journal of Medical Physics
دوره 21، شماره 4، مهر و آبان 2024، صفحه 237-248    اصل مقاله (1.93 M)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22038/ijmp.2023.71683.2268
نویسندگان
Mohamad Firouzmand1؛ Keivan Majidzadeh2؛ Maryam Jafari3؛ Shahpar Haghighat4؛ Rezvan Esmaeili5؛ Leila Moradi1؛ Nima Misaghi6؛ Mahsa Ensafi7؛ Fatemeh Batmanghelich6؛ Mohammadreza Keyvanpour7؛ Seyed Vahab Shojaedini* 8
1Department of Biomedical Engineering, Iranian Research Organization for Science & Technology (IROST), Tehran, Iran
2Biomaterials and Tissue Engineering Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
3Breast Diseases Department, Motamed Cancer Institute, South Gandhi St., Tehran, IR Iran.
4Iranian Centre for Breast Cancer (ICBC), ACECR, Tehran, Iran
5Genetics Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, South Gandhi, Vanak Square, Tehran 1517964311, Iran.
6Department of Computer Engineering, Faculty of Engineering, Islamic Azad University E-Campus, Tehran, Iran
7Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
8Electrical Engineering Department, Iranian Research Organization for Science and Technology, Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Breast Cancer؛ Medical Imaging؛ Artificial Intelligence؛ Deep Learning؛ Temperature Mapping
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