Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images
Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier i...
- Autores:
-
Escorcia-Gutierrez, Jose
Mansour, Romany F.
Beleño, Kelvin
Jiménez-Cabas, Javier
Pérez, Meglys
Madera, Natasha
Velasquez, Kevin
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9076
- Acceso en línea:
- https://hdl.handle.net/11323/9076
https://repositorio.cuc.edu.co/
- Palabra clave:
- Breast cancer
Digital mammograms
Deep learning
Wavelet neural network
Resnet 34
Disease diagnosis
- Rights
- openAccess
- License
- Copyright© 2020 Tech Science Press
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Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images |
| title |
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images |
| spellingShingle |
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images Breast cancer Digital mammograms Deep learning Wavelet neural network Resnet 34 Disease diagnosis |
| title_short |
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images |
| title_full |
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images |
| title_fullStr |
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images |
| title_full_unstemmed |
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images |
| title_sort |
Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, Jose Mansour, Romany F. Beleño, Kelvin Jiménez-Cabas, Javier Pérez, Meglys Madera, Natasha Velasquez, Kevin |
| dc.contributor.author.spa.fl_str_mv |
Escorcia-Gutierrez, Jose Mansour, Romany F. Beleño, Kelvin Jiménez-Cabas, Javier Pérez, Meglys Madera, Natasha Velasquez, Kevin |
| dc.subject.proposal.eng.fl_str_mv |
Breast cancer Digital mammograms Deep learning Wavelet neural network Resnet 34 Disease diagnosis |
| topic |
Breast cancer Digital mammograms Deep learning Wavelet neural network Resnet 34 Disease diagnosis |
| description |
Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures. |
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2022 |
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2022-03-14T22:03:27Z |
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2022-03-14T22:03:27Z |
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2022 |
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Artículo de revista |
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Escorcia-Gutierrez, J., Mansour, R. F., Beleño, K., Jiménez-Cabas, J., Pérez, M. et al. (2022). Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images. CMC-Computers, Materials & Continua, 71(3), 4221–4235. |
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1546-2218 |
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https://hdl.handle.net/11323/9076 |
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10.32604/cmc.2022.022322 |
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1546-2226 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Escorcia-Gutierrez, J., Mansour, R. F., Beleño, K., Jiménez-Cabas, J., Pérez, M. et al. (2022). Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images. CMC-Computers, Materials & Continua, 71(3), 4221–4235. 1546-2218 10.32604/cmc.2022.022322 1546-2226 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/9076 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Computers, Materials and Continua |
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[1] S. K. Lakshmanaprabu, S. N. Mohanty, K. Shankar, N. Arunkumar and G. Ramireze, “Optimal deep learning model for classification of lung cancer on CT images,” Future Generation Computer Systems, vol.92, no. 1, pp. 374–382, 2019. [2] I. V. Pustokhina, D. A. Pustokhin, T. Vaiyapuri, D. Gupta, S. Kumar et al., “An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety,”Safety Science, vol. 142, pp. 105356, 2021. [3] K. Shankar, E. Perumal, P. Tiwari, M. Shorfuzzaman and D. Gupta, “Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images,” Multimedia Systems, vol. 66, no. 2, pp. 1921, 2021. [4] I. V. Pustokhina, D. A. Pustokhin, P. K. Pareek, D. Gupta, A. Khanna et al., “Energy-efficient clusterbased unmanned aerial vehicle networks with deep learning-based scene classification model,”International Journal of Communication Systems, vol. 34, no. 8, pp. 1–16, 2021. [5] T. Vaiyapuri, S. N. Mohanty, M. Sivaram, I. V. Pustokhina, D. A. Pustokhin et al., “Automatic vehicle license plate recognition using optimal deep learning model,” Computers, Materials & Continua, vol. 67, no. 2, pp. 1881–1897, 2021. [6] R. F. Mansour, A. El Amraoui, I. Nouaouri, V. G. Díaz, D. Gupta et al., “Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems,” IEEE Access, vol. 9, pp. 45137– 45146, 2021. [7] L. Li, L. Sun, Y. Xue, S. Li, X. Huang et al., “Fuzzy multilevel image thresholding based on improved coyote optimization algorithm,” IEEE Access, vol. 9, pp. 33595–33607, 2021. [8] R. F. Mansour, J. E. Gutierrez, M. Gamarra, V. García, D. Gupta et al., “Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images,” Neural Computing and Applications, pp. 1–13, 2021. https://doi.org/10.1007/s00521-021-06240-y. [9] R. F. Mansour, “A robust deep neural network based breast cancer detection and classification,” International Journal of Computational Intelligence and Applications, vol. 19, no. 1, pp. 2050007, 2020. [10] R. F. Mansour, “Evolutionary computing enriched ridge regression model for craniofacial reconstruction,” Multimedia Tools and Applications, vol. 79, no. 31, pp. 22065–22082, 2020. [11] C. Zhang, J. Zhao, J. Niu and D. Li, “New convolutional neural network model for screening and diagnosis of mammograms,” PLoS ONE, vol. 15, no. 8, pp. e0237674, 2020. [12] M. Altaf, “A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulsecoupled neural networks,” Mathematical Biosciences and Engineering, vol. 18, no. 5, pp. 5029–5046, 2021. [13] A. Yala, C. Lehman, T. Schuster, T. Portnoi and R. Barzilay, “A deep learning mammography-based model for improved breast cancer risk prediction,” Radiology, vol. 292, no. 1, pp. 60–66, 2019. [14] L. Shen, L. R. Margolies, J. H. Rothstein, E. Fluder, R. McBride et al., “Deep learning to improve breast cancer detection on screening mammography,” Scientific Reports, vol. 9, no. 1, pp. 12495, 2019. [15] A. Kumar, S. Mukherjee and A. K. Luhach, “Deep learning with perspective modeling for early detection of malignancy in mammograms,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 4, pp. 627–643, 2019. [16] P. Kaur, G. Singh and P. Kaur, “Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification,” Informatics in Medicine Unlocked, vol. 16, no. 1, pp. 100239, 2019. [17] S. S. Aboutalib, A. A. Mohamed, W. A. Berg, M. L. Zuley, J. H. Sumkin et al., “Deep learning to distinguish recalled but benign mammography images in breast cancer screening,” Clinical Cancer Research, vol. 24, no. 23, pp. 5902–5909, 2018. [18] M. A. A. Masni, M. A. A. Antari, J. M. Park, G. Gi, T. Y. Kim et al., “Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system,” Computer Methods and Programs in Biomedicine, vol. 157, no. 1, pp. 85–94, 2018. [19] M. A. Al-antari, S. M. Han and T. S. Kim, “Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms,” Computer Methods and Programs in Biomedicine, vol. 196, pp. 105584, 2020. [20] S. Punitha, A. Amuthan and K. S. Joseph, “Benign and malignant breast cancer segmentation using optimized region growing technique,” Future Computing and Informatics Journal, vol. 3, no. 2, pp. 348– 358, 2018. [21] V. Rajinikanth, S. C. Satapathy, S. L. Fernandes and S. Nachiappan, “Entropy based segmentation of tumor from brain MR images–A study with teaching learning based optimization,” Pattern Recognition Letters, vol. 94, no. 1, pp. 87–95, 2017. [22] A. Helwan, M. K. S. Ma”aitah, R. H. Abiyev, S. Uzelaltinbulat and B. Sonyel, “Deep learning based on residual networks for automatic sorting of bananas,” Journal of Food Quality, vol. 2021, pp. 1–11, 2021. [23] A. A. Nahid, M. A. Mehrabi and Y. Kong, “Histopathological breast cancer image classification by deep neural network techniques guided by local clustering,”BioMed Research International, vol. 2018, pp. 1–20, 2018. [24] M. Khishe and M. R. Mosavi, “Classification of underwater acoustical dataset using neural network trained by chimp optimization algorithm,” Applied Acoustics, vol. 157, no. 13, pp. 107005, 2020. [25] L. L. S. Linhares, A. I. R. Fontes, A. M. Martins, F. M. U. Araújo and L. F. Q. Silveira, “Fuzzy wavelet neural network using a correntropy criterion for nonlinear system identification,” Mathematical Problems in Engineering, vol. 2015, no. 5, pp. 1–12, 2015. [26] Dataset. [Online]. Available: http://peipa.essex.ac.uk/info/mias.html. |
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Escorcia-Gutierrez, JoseMansour, Romany F.Beleño, KelvinJiménez-Cabas, JavierPérez, MeglysMadera, NatashaVelasquez, Kevin2022-03-14T22:03:27Z2022-03-14T22:03:27Z2022Escorcia-Gutierrez, J., Mansour, R. F., Beleño, K., Jiménez-Cabas, J., Pérez, M. et al. (2022). Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images. CMC-Computers, Materials & Continua, 71(3), 4221–4235.1546-2218https://hdl.handle.net/11323/907610.32604/cmc.2022.0223221546-2226Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.15 páginasapplication/pdfengTech Science PressUnited StatesCopyright© 2020 Tech Science PressAtribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Automated deep learning empowered breast cancer diagnosis using biomedical mammogram imagesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.techscience.com/cmc/v71n3/46473Computers, Materials and Continua[1] S. K. Lakshmanaprabu, S. N. Mohanty, K. Shankar, N. Arunkumar and G. Ramireze, “Optimal deep learning model for classification of lung cancer on CT images,” Future Generation Computer Systems, vol.92, no. 1, pp. 374–382, 2019.[2] I. V. Pustokhina, D. A. Pustokhin, T. Vaiyapuri, D. Gupta, S. Kumar et al., “An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety,”Safety Science, vol. 142, pp. 105356, 2021.[3] K. Shankar, E. Perumal, P. Tiwari, M. Shorfuzzaman and D. Gupta, “Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images,” Multimedia Systems, vol. 66, no. 2, pp. 1921, 2021.[4] I. V. Pustokhina, D. A. Pustokhin, P. K. Pareek, D. Gupta, A. Khanna et al., “Energy-efficient clusterbased unmanned aerial vehicle networks with deep learning-based scene classification model,”International Journal of Communication Systems, vol. 34, no. 8, pp. 1–16, 2021.[5] T. Vaiyapuri, S. N. Mohanty, M. Sivaram, I. V. Pustokhina, D. A. Pustokhin et al., “Automatic vehicle license plate recognition using optimal deep learning model,” Computers, Materials & Continua, vol. 67, no. 2, pp. 1881–1897, 2021.[6] R. F. Mansour, A. El Amraoui, I. Nouaouri, V. G. Díaz, D. Gupta et al., “Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems,” IEEE Access, vol. 9, pp. 45137– 45146, 2021.[7] L. Li, L. Sun, Y. Xue, S. Li, X. Huang et al., “Fuzzy multilevel image thresholding based on improved coyote optimization algorithm,” IEEE Access, vol. 9, pp. 33595–33607, 2021.[8] R. F. Mansour, J. E. Gutierrez, M. Gamarra, V. García, D. Gupta et al., “Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images,” Neural Computing and Applications, pp. 1–13, 2021. https://doi.org/10.1007/s00521-021-06240-y.[9] R. F. Mansour, “A robust deep neural network based breast cancer detection and classification,” International Journal of Computational Intelligence and Applications, vol. 19, no. 1, pp. 2050007, 2020.[10] R. F. Mansour, “Evolutionary computing enriched ridge regression model for craniofacial reconstruction,” Multimedia Tools and Applications, vol. 79, no. 31, pp. 22065–22082, 2020.[11] C. Zhang, J. Zhao, J. Niu and D. Li, “New convolutional neural network model for screening and diagnosis of mammograms,” PLoS ONE, vol. 15, no. 8, pp. e0237674, 2020.[12] M. Altaf, “A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulsecoupled neural networks,” Mathematical Biosciences and Engineering, vol. 18, no. 5, pp. 5029–5046, 2021.[13] A. Yala, C. Lehman, T. Schuster, T. Portnoi and R. Barzilay, “A deep learning mammography-based model for improved breast cancer risk prediction,” Radiology, vol. 292, no. 1, pp. 60–66, 2019.[14] L. Shen, L. R. Margolies, J. H. Rothstein, E. Fluder, R. McBride et al., “Deep learning to improve breast cancer detection on screening mammography,” Scientific Reports, vol. 9, no. 1, pp. 12495, 2019.[15] A. Kumar, S. Mukherjee and A. K. Luhach, “Deep learning with perspective modeling for early detection of malignancy in mammograms,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 4, pp. 627–643, 2019.[16] P. Kaur, G. Singh and P. Kaur, “Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification,” Informatics in Medicine Unlocked, vol. 16, no. 1, pp. 100239, 2019.[17] S. S. Aboutalib, A. A. Mohamed, W. A. Berg, M. L. Zuley, J. H. Sumkin et al., “Deep learning to distinguish recalled but benign mammography images in breast cancer screening,” Clinical Cancer Research, vol. 24, no. 23, pp. 5902–5909, 2018.[18] M. A. A. Masni, M. A. A. Antari, J. M. Park, G. Gi, T. Y. Kim et al., “Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system,” Computer Methods and Programs in Biomedicine, vol. 157, no. 1, pp. 85–94, 2018.[19] M. A. Al-antari, S. M. Han and T. S. Kim, “Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms,” Computer Methods and Programs in Biomedicine, vol. 196, pp. 105584, 2020.[20] S. Punitha, A. Amuthan and K. S. Joseph, “Benign and malignant breast cancer segmentation using optimized region growing technique,” Future Computing and Informatics Journal, vol. 3, no. 2, pp. 348– 358, 2018.[21] V. Rajinikanth, S. C. Satapathy, S. L. Fernandes and S. Nachiappan, “Entropy based segmentation of tumor from brain MR images–A study with teaching learning based optimization,” Pattern Recognition Letters, vol. 94, no. 1, pp. 87–95, 2017.[22] A. Helwan, M. K. S. Ma”aitah, R. H. Abiyev, S. Uzelaltinbulat and B. Sonyel, “Deep learning based on residual networks for automatic sorting of bananas,” Journal of Food Quality, vol. 2021, pp. 1–11, 2021.[23] A. A. Nahid, M. A. Mehrabi and Y. Kong, “Histopathological breast cancer image classification by deep neural network techniques guided by local clustering,”BioMed Research International, vol. 2018, pp. 1–20, 2018.[24] M. Khishe and M. R. Mosavi, “Classification of underwater acoustical dataset using neural network trained by chimp optimization algorithm,” Applied Acoustics, vol. 157, no. 13, pp. 107005, 2020.[25] L. L. S. Linhares, A. I. R. Fontes, A. M. Martins, F. M. U. Araújo and L. F. Q. Silveira, “Fuzzy wavelet neural network using a correntropy criterion for nonlinear system identification,” Mathematical Problems in Engineering, vol. 2015, no. 5, pp. 1–12, 2015.[26] Dataset. [Online]. Available: http://peipa.essex.ac.uk/info/mias.html.42213714235Breast cancerDigital mammogramsDeep learningWavelet neural networkResnet 34Disease diagnosisPublicationORIGINALAutomated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images.pdfAutomated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images.pdfapplication/pdf752404https://repositorio.cuc.edu.co/bitstreams/66b40124-219b-4341-9804-88a25370abc5/download4dfe2850293465f76917fb482402b984MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/e81b7149-36ae-4615-8684-a0e6e1e53c1d/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTAutomated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images.pdf.txtAutomated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram 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