Breast cancer detection using infrared thermal imaging and a deep learning model
Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preser...
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my.utm.863792020-08-31T14:02:35Z http://eprints.utm.my/id/eprint/86379/ Breast cancer detection using infrared thermal imaging and a deep learning model Mambou, Sebastien Jean Maresova, Petra Krejcar, Ondrej Selamat, Ali Kuca, Kamil QA75 Electronic computers. Computer science Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models. MDPI AG 2018-08-25 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/86379/1/AliSelamat2018_BreastCancerDetectionUsingInfrared.pdf Mambou, Sebastien Jean and Maresova, Petra and Krejcar, Ondrej and Selamat, Ali and Kuca, Kamil (2018) Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors, 18 (9). ISSN 1424-8220 http://dx.doi.org/10.3390/s18092799 DOI:10.3390/s18092799 |
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QA75 Electronic computers. Computer science Mambou, Sebastien Jean Maresova, Petra Krejcar, Ondrej Selamat, Ali Kuca, Kamil Breast cancer detection using infrared thermal imaging and a deep learning model |
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Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models. |
format |
Article |
author |
Mambou, Sebastien Jean Maresova, Petra Krejcar, Ondrej Selamat, Ali Kuca, Kamil |
author_facet |
Mambou, Sebastien Jean Maresova, Petra Krejcar, Ondrej Selamat, Ali Kuca, Kamil |
author_sort |
Mambou, Sebastien Jean |
title |
Breast cancer detection using infrared thermal imaging and a deep learning model |
title_short |
Breast cancer detection using infrared thermal imaging and a deep learning model |
title_full |
Breast cancer detection using infrared thermal imaging and a deep learning model |
title_fullStr |
Breast cancer detection using infrared thermal imaging and a deep learning model |
title_full_unstemmed |
Breast cancer detection using infrared thermal imaging and a deep learning model |
title_sort |
breast cancer detection using infrared thermal imaging and a deep learning model |
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MDPI AG |
publishDate |
2018 |
url |
http://eprints.utm.my/id/eprint/86379/1/AliSelamat2018_BreastCancerDetectionUsingInfrared.pdf http://eprints.utm.my/id/eprint/86379/ http://dx.doi.org/10.3390/s18092799 |
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