Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers
DOI:
https://doi.org/10.21015/vtse.v11i4.1684Abstract
Breast cancer is one of the most prevalent and life-threatening forms of cancer due to its aggressive nature and high mortality rates. Early detection significantly improves a patient's chances of survival. Currently, mammography is the preferred diagnostic method, but it has drawbacks such as radiation exposure and high costs. In response to these challenges, thermography has become a less invasive and cost-effective alternative, gaining popularity. We aim to develop a cutting-edge model for breast cancer detection based on thermal imaging. The initial phase involves creating a customized machine-learning (ML) model built on convolutional neural networks (CNN). Subsequently, this model undergoes training using a diverse dataset of thermal images depicting breast abnormalities, enabling it to identify breast cancer effectively. This innovative approach promises to revolutionize breast cancer diagnosis and offers a safer and more accessible alternative to traditional methods. In our recent study, we leveraged thermal image processing techniques to forecast breast cancer precisely based on its external manifestations, particularly in cases where multiple factors are interconnected. This research employed various image classification methods to categorize breast cancer effectively. Our comprehensive approach encompassed segmentation, texture-based feature extraction from thermal images, and subsequent image classification, leading to the successful detection of malignant images. Our study harnessed the power of machine learning to create a tailored classifier, merging key components from GoogleNet, including the utilization of 2D CNNs and activation functions, with the ResNet architecture. This hybrid approach incorporated batch normalization layers following each convolutional layer and employed max-pooling to enhance classification accuracy. Next, we used a sample dataset of carefully selected images from DMR-IR to train our proposed model. The outcomes of this training demonstrated significant improvement over existing methods, with our suggested 2D CNN classifiers achieving an impressive classification rate of 95%, surpassing both the SVM and current CNN models, which achieved rates of 91% and 71%, respectively.
References
{1}
American cancer society.about breast cancer.org— 1.800.227.2345. emph{WHO} 2020.
{2}
N. Dey, A. S. Ashour, and A. S. Althoupety, "Thermal imaging in medical science," in emph{Recent Advances in Applied Thermal Imaging for Industrial Applications}, 2017, pp. 87-117. DOI: https://doi.org/10.4018/978-1-5225-2423-6.ch004
{3}
E. Daniela and M. Reynoso, "Diagnosis of breast cancer through the emph{processing of thermographic images and neural networks}," 2017.
{4}
M. Waqas, M. A. Tahir, and R. Qureshi, "Ensemble-based instance relevance estimation in multiple-instance learning," emph{in 2021 9th European Workshop on Visual Information Processing (EUVIP)}, 2021, pp. 1-6.
{5}
M. Waqas, M. A. Tahir, and R. Qureshi, "Deep Gaussian mixture model based instance relevance estimation for multiple instance learning applications," emph{Applied intelligence}, vol. 53, no. 9, pp. 10310-10325, 2023.
{6}
M. Waqas, M. A. Tahir, and S. A. Khan, "Robust bag classification approach for multi-instance learning via subspace fuzzy clustering," emph{Expert Systems with Applications}, vol. 214, p. 119113, 2023.
{7}
S. T. Kakileti et al., "Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics," emph{Artificial Intelligence in Medicine}, vol. 105, p. 101854, 2020.
{8}
M. Hanif, M. Waqas, A. Muneer, A. Alwadain, M. A. Tahir, and M. Rafi, "DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events," emph{Sustainability}, vol. 15, no. 7, p. 6049, 2023.
{9}
R. Roslidar, A. Rahman, R. Muharar, M. R. Syahputra, F. Arnia, M. Syukri, et al., "A review on recent progress in thermal imaging and deep learning approaches for breast cancer detection," emph{IEEE Access}, vol. 8, pp. 116176-116194, 2020.
{10}
S. Ansari and S. Salankar, "An Overview on Thermal Image Processing," emph{in Proc. Second Int. Conf. Res. Intell. Comput. Eng}., 2017, vol. 10, pp. 117–120. DOI: https://doi.org/10.15439/2017R111
{11}
A. Akula, R. Ghosh, and H. K. Sardana, "Thermal imaging and its application in defence systems," emph{AIP Conf. Proc}., vol. 1391, pp. 333–335, 2011, doi: 10.1063/1.3643540. DOI: https://doi.org/10.1063/1.3643540
{12}
R. Vardasca, L. Vaz, and J. Mendes, "Classification and decision making of medical infrared thermal images," emph{in Lect. Notes Comput. Vis. Biomech}., 2018, vol. 26, pp. 79–104. DOI: https://doi.org/10.1007/978-3-319-65981-7_4
{13}
B. B. Lahiri, S. Bagavathiappan, T. Jayakumar, and J. Philip, "Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , emph{the company ’ s public news and information} ," no. January, 2020.
{14}
A. Duarte et al., "Segmentation Algorithms for Thermal Images," emph{Procedia Technol}., vol. 16, pp. 1560–1569, 2014, doi: 10.1016/j.protcy.2014.10.178. DOI: https://doi.org/10.1016/j.protcy.2014.10.178
{15}
D. Singh and A. K. Singh, "Role of image thermography in early breast cancer detection- Past, present and future," emph{Comput. Methods Programs Biomed.}, vol. 183, 2020, doi: 10.1016/j.cmpb.2019.105074.
{16}
R. Resmini, A. Conci, U. Federalfluminense, A. Silva, E. Diniz, and U. F. Fluminense, "THERMAL FEATURE ANALYSIS TO AID ON BREAST DISEASE," no. March 2016, 2011.
{17}
M. A. Farooq and P. Corcoran, "Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images," emph{in 2020 31st Irish Signals Syst. Conf. ISSC 2020}, 2020, doi: 10.1109/ISSC49989.2020.9180164.
{18}
A. Al Nahid and Y. Kong, "Involvement of Machine Learning for Breast Cancer Image Classification: A Survey," emph{Comput. Math. Methods Med}., vol. 2017, no. i, 2017, doi: 10.1155/2017/3781951. DOI: https://doi.org/10.1155/2017/3781951
{19}
F. Alfayez, M. W. A. El-soud, and T. Gaber, "Thermogram Breast Cancer Detection : A Comparative Study of Two Machine Learning Techniques," 2020, doi: 10.3390/app10020551.
{20}
M. A. S. Al Husaini, M. H. Habaebi, T. S. Gunawan, M. R. Islam, and S. A. Hameed, "Automatic Breast Cancer Detection Using Inception V3 in Thermography," pp. 255–258, 2021, doi: 10.1109/iccce50029.2021.9467231.
{21}
P. S. Pawar and D. R. Patil, "Breast cancer detection using neural network models," in Proc.- 2013 Int. Conf. Commun. emph{Syst. Netw. Technol. CSNT} 2013, 2013, pp. 568–572. DOI: https://doi.org/10.1109/CSNT.2013.122
{22}
I. Dabral, M. Singh, and K. Kumar, "Cancer Detection Using Convolutional Neural Network," in Lect. emph{Notes Networks Syst}., 2021, vol. 175, pp. 290–298.
{23}
J. Zuluaga-Gomez, Z. Al Masry, K. Benaggoune, S. Meraghni, and N. Zerhouni, "A CNN- based methodology for breast cancer diagnosis using thermal images," emph{Comput. Methods Biomech. Biomed. Eng. Imaging Vis}., vol. 9, no.
{24}
J. C. Torres-Galván, E. Guevara, and F. J. González, "Comparison of Deep Learning Architectures For Pre-Screening of Breast Cancer Thermograms," emph{2018 IEEE International Symposium on Computer Applications & Industrial Electronics (ISCAIE)}, pp. 2–3, 2019. doi: 10.1109/PN.2019.8819587.
{25}
S. J. Mambou, P. Maresova, O. Krejcar, A. Selamat, and K. Kuca, "Breast cancer detection using infrared thermal imaging and a deep learning model," emph{Sensors (Switzerland)}, vol. 18, no. 9, Sep. 2018. doi: 10.3390/s18092799.
{26}
G. J. Zuluaga, Z. Al Masry, K. Benaggoune, S. Meraghni, and N. Zerhouni, "A CNN-based methodology for breast cancer diagnosis using thermal images," emph{Comput Methods Biomech Biomed Eng Imaging Vis}, 2020. doi: 10.1080/21681163.2020.1824685.
{27}
G. I. Sayed, M. Soliman, and A. E. Hassanien, "Bio-inspired swarm techniques for thermogram breast cancer detection," in Medical Imaging in Clinical Applications: Algorithmic and Computer-Based Approaches, pp. 487–506, emph{Springer International Publishing}, 2016. DOI: https://doi.org/10.1007/978-3-319-33793-7_21
{28}
A. Hakim and R. N. Awale, "Harnessing the power of machine learning for breast anomaly prediction using thermograms," emph{International Journal of Medical Engineering and Informatics}, vol. 15, no. 1, pp. 1-22, 2023.
{29}
R. Karthiga and K. Narasimhan, "Medical imaging technique using curvelet transform and machine learning for the automated diagnosis of breast cancer from thermal image," emph{Pattern Analysis and Applications}, vol. 24, no. 3, pp. 981-991, 2021.
{30}
M. J. Mammoottil, L. J. Kulangara, A. S. Cherian, P. Mohandas, K. Hasikin, and M. Mahmud, "Detection of breast cancer from five-view thermal images using convolutional neural networks," emph{Journal of Healthcare Engineering}, 2022.
{31}
J. Zuluaga-Gomez, Z. Al Masry, K. Benaggoune, S. Meraghni, and N. Zerhouni, "A CNN-based methodology for breast cancer diagnosis using thermal images," emph{Computer Methods in Biomechanics and Biomedical Engineering}: Imaging & Visualization, vol. 9, no. 2, pp. 131-145, 2021.
{32}
S. U. R. Khan, M. Zhao, S. Asif, X. Chen, and Y. Zhu, "GLNET: global–local CNN's-based informed model for detection of breast cancer categories from histopathological slides," emph{The Journal of Supercomputing}, 2023. doi: [DOI to be added when available].
{33}
S. U. R. Khan, M. Zhao, S. Asif, and X. Chen, "Hybrid-NET: A fusion of DenseNet169 and advanced machine learning classifiers for enhanced brain tumor diagnosis," emph{International Journal of Imaging Systems and Technology}, doi: 10.1002/ima.22975.
{34}
S. Chatterjee, S. Biswas, A. Majee, S. Sen, D. Oliva, and R. Sarkar, "Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method," emph{Computers in Biology and Medicine}, vol. 141, 105027, 2022.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
