A Literature Review: Detection of COVID-19 in Computed Tomography Images Using Deep Learning
Resumo
Through the development of the COVID-19 disease, various diagnosis methods have been studied. One of them is the computed tomography (CT), which has the best level of detail among medical image exams. The CT generates a repeatable and tiring workload, in addition to needing a team that is familiar with the findings that indicate pneumonia caused by COVID-19. To reduce this manual work and collaborate with these teams, several studies have been carried out using deep learning techniques. In this way, this study presents a review of the literature regarding the detection of COVID-19 in CT that uses deep learning to collaborate with a theoretical basis for future works.
Referências
Abdar, A. K., Sadjadi, S. M., Soltanian-Zadeh, H., Bashirgonbadi, A., and Naghibi, M. (2020). Automatic detection of coronavirus (covid-19) from chest ct images using vgg16-based deep-learning. In 2020 5th ICBME, pages 212–216.
Ardakani, A., Kana, A., Acharya, U., Khadem, N., and Mohammadi, A. (2020). Application of deep learning technique to manage covid-19 in routine clinical practice using ct images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121:103795.
Arora, V., Ng, E., Leekha, R., Darshan, M., and Singh, A. (2021). Transfer learning-based approach for detecting covid-19 ailment in lung ct scan. Computers in Biology and Medicine, 135:104575.
Berrimi, M., Hamdi, S., Cherif, R. Y., Moussaoui, A., Oussalah, M., and Chabane, M. (2021). Covid-19 detection from xray and ct scans using transfer learning. In International Conference of Women in Data Science at Taif University, pages 1–6.
Cai, X., Wang, Y., Sun, X., Liu, W., Tang, Y., and Li, W. (2020). Comparing the performance of resnets on covid-19 diagnosis using ct scans. In 2020 CITS, pages 1–4.
Carvalho, E., Carvalho, E., Carvalho Filho, A., de Araújo, F., and Rabêlo, R. (2020). Diagnosis of covid-19 in ct image using cnn and xgboost. In 2020 ISCC, pages 1–6.
Castiglione, A., Vijayakumar, P., Nappi, M., Sadiq, S., and Umer, M. (2021). Covid-19: Automatic detection of the novel coronavirus disease from ct images using an optimized convolutional neural network. IEEE Trans. Ind. Informat., 17(9):6480–6488.
Cruz, J. (2021). An ensemble approach for multi-stage transfer learning models for covid19 detection from chest ct scans. volume 5, page 100027. Intelligence-Based Medicine.
Dan-Sebastian, B., Delia-Alexandrina, M., Sergiu, N., and Radu, B. (2020). Adversarial graph learning and deep learning techniques for improving diagnosis within ct and ultrasound images. In 2020 IEEE 16th ICCP, pages 449–456.
Di, D., Shi, F., Yan, F., Xia, L., Mo, Z., Ding, Z., Shan, F., Song, B., Li, S., Wei, Y., Shao, Y., Han, M., Gao, Y., Sui, H., Gao, Y., and Shen, D. (2021). Hypergraph learning for identification of covid-19 with ct imaging. Medical Image Analysis, 68:101910.
Dutta, P., Roy, T., and Anjum, N. (2021). Covid-19 detection using transfer learning with convolutional neural network. In 2021 2nd ICREST, pages 429–432.
Farias, L., Strabelli, D., Fonseca, E., Loureiro, B., Nomura, C., and Sawamura, M. (2020). Alterações tomográficas torácicas em pacientes sintomáticos respiratórios com a covid19. volume 53. Radiologia Brasileira.
Gifani, P., Shalbaf, A., and Vafaeezadeh, M. (2021). Automated detection of covid-19 using ensemble of transfer learning with deep convolutional. International Journal of Computer Assisted Radiology and Surgery, 16.
Gunraj, H., Wang, L., and Wong, A. (2020). Covidnet-ct: A tailored deep convolutional neural network design for detection of covid-19 cases from chest ct images. Frontiers in Medicine, 7:1025.
Hu, K., Huang, Y., Huang, W., Tan, H., Chen, Z., Zhong, Z., Li, X., Zhang, Y., and Gao, X. (2021). Deep supervised learning using self-adaptive auxiliary loss for covid-19 diagnosis from imbalanced ct images. volume 458, pages 232–245.
Islam, M. R. and Matin, A. (2020). Detection of covid 19 from ct image by the novel lenet-5 cnn architecture. In 2020 23rd ICCIT, pages 1–5.
Jia, G., Lam, H.-K., and Xu, Y. (2021). Classication of covid-19 chest x-ray and ct images using a type of dynamic cnn modication method. Computers in Biology and Medicine, 134:104425.
Júnior, E. A. and Yamashita, H. (Maio 2001). Aspectos básicos de tomografia computadorizada e ressonância magnética. Brazilian Journal of Psychiatry, 23.
Kamel, M. A., Abdelshafy, M., AbdulRazek, M., Abouelkhir, O., Fawzy, A., and Sahlol, A. T. (2021). Efficient classification approach based on covid-19 ct images analysis with deep features. In 2021 ICCCIS, pages 459–464.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Lai, Yaoming e Li, G., Wu, D., Lian, W., Li, C., Tian, J., Ma, X., Chen, H., Xu, W., Wei, J., Zhang, Y., and Jiang, G. (2020). 2019 novel coronavirus-infected pneumonia on ct: A feasibility study of few-shot learning for computerized diagnosis of emergency diseases. IEEE, 8:194158–194165.
Li, C., Yang, Y., Liang, H., and Wu, B. (2021). Transfer learning for establishment of recognition of covid-19 on ct imaging using small-sized training datasets. Knowledge-Based Systems, 218:106849.
Mahase, E. (2020). Coronavirus: covid-19 has killed more people than sars and mers combined, despite lower case fatality rate. volume 368. BMJ.
Mishra, N., Singh, P., and Joshi, S. (2021). Automated detection of covid-19 from ct scan using convolutional neural network. Biocybernetics and Biomedical Engineering, 41:572–588.
Organization, W. H. (2020). Coronavirus. World Health Organization.
Ponti, M. A. and da Costa, G. B. P. (Tópicos em Gerenciamento de Dados e Informações, 2017). Como funciona o deep learning. SBC, 1a ed., 23.
Rahimzadeh, M., Attar, A., and Sakhaei, S. M. (2021). A fully automated deep learning-based network for detecting covid-19 from a new and large lung ct scan dataset. In Biomedical Signal Processing and Control, page 102588.
Sari, I. P., Widodo, Nugraheni, M., and Wanda, P. (2020). A basic concept of image classification for covid-19 patients using chest ct scan and convolutional neural network. In 2020 1st ICITAMEE, pages 175–178.
Serte, S. and Serener, A. (2020). Discerning covid-19 from mycoplasma and viral pneumonia on ct images via deep learning. In 2020 4th ISMSIT, pages 1–5.
Seum, A., Raj, A. H., Sakib, S., and Hossain, T. (2020). A comparative study of cnn transfer learning classication algorithms with segmentation for covid-19 detection from ct scan images. In 2020 11th ICECE, pages 234–237.
Turkoglu, M. (2021). Covid-19 detection system using chest ct images and multiple kernels-extreme learning machine based on deep neural network. IRBM, 42:207–214.
Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., and Tan, W. (2020a). Detection of SARS-CoV-2 in Different Types of Clinical Specimens. pages 1843–1844. JAMA.
Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., and Zheng, C. (2020b). A weakly supervised framework for covid-19 classication and lesion localization from chest ct. IEEE Transactions on Medical Imaging, 39(8):2615–2625.
Wang, Z., Liu, Q., and Dou, Q. (2020c). Contrastive cross-site learning with redesigned net for covid-19 ct classication. IEEE J. Biomed. Health Inform., 24(10):2806–2813.
Yener, F. M. and Oktay, A. B. (2020). Diagnosis of covid-19 with a deep learning approach on chest ct slices. In 2020 TIPTEKNO, pages 1–4.
Zhang, Y.-D., Satapathy, S. C., Zhu, L.-Y., Górriz, J. M., and Wang, S.-H. (2020). A seven-layer convolutional neural network for chest ct based covid-19 diagnosis using stochastic pooling. pages 1–1. IEEE Sensors Journal.
Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., and Dong, Y. (2021). The ensemble deep learning model for novel covid-19 on ct images. Applied Soft Computing, 98:106885.
Ardakani, A., Kana, A., Acharya, U., Khadem, N., and Mohammadi, A. (2020). Application of deep learning technique to manage covid-19 in routine clinical practice using ct images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121:103795.
Arora, V., Ng, E., Leekha, R., Darshan, M., and Singh, A. (2021). Transfer learning-based approach for detecting covid-19 ailment in lung ct scan. Computers in Biology and Medicine, 135:104575.
Berrimi, M., Hamdi, S., Cherif, R. Y., Moussaoui, A., Oussalah, M., and Chabane, M. (2021). Covid-19 detection from xray and ct scans using transfer learning. In International Conference of Women in Data Science at Taif University, pages 1–6.
Cai, X., Wang, Y., Sun, X., Liu, W., Tang, Y., and Li, W. (2020). Comparing the performance of resnets on covid-19 diagnosis using ct scans. In 2020 CITS, pages 1–4.
Carvalho, E., Carvalho, E., Carvalho Filho, A., de Araújo, F., and Rabêlo, R. (2020). Diagnosis of covid-19 in ct image using cnn and xgboost. In 2020 ISCC, pages 1–6.
Castiglione, A., Vijayakumar, P., Nappi, M., Sadiq, S., and Umer, M. (2021). Covid-19: Automatic detection of the novel coronavirus disease from ct images using an optimized convolutional neural network. IEEE Trans. Ind. Informat., 17(9):6480–6488.
Cruz, J. (2021). An ensemble approach for multi-stage transfer learning models for covid19 detection from chest ct scans. volume 5, page 100027. Intelligence-Based Medicine.
Dan-Sebastian, B., Delia-Alexandrina, M., Sergiu, N., and Radu, B. (2020). Adversarial graph learning and deep learning techniques for improving diagnosis within ct and ultrasound images. In 2020 IEEE 16th ICCP, pages 449–456.
Di, D., Shi, F., Yan, F., Xia, L., Mo, Z., Ding, Z., Shan, F., Song, B., Li, S., Wei, Y., Shao, Y., Han, M., Gao, Y., Sui, H., Gao, Y., and Shen, D. (2021). Hypergraph learning for identification of covid-19 with ct imaging. Medical Image Analysis, 68:101910.
Dutta, P., Roy, T., and Anjum, N. (2021). Covid-19 detection using transfer learning with convolutional neural network. In 2021 2nd ICREST, pages 429–432.
Farias, L., Strabelli, D., Fonseca, E., Loureiro, B., Nomura, C., and Sawamura, M. (2020). Alterações tomográficas torácicas em pacientes sintomáticos respiratórios com a covid19. volume 53. Radiologia Brasileira.
Gifani, P., Shalbaf, A., and Vafaeezadeh, M. (2021). Automated detection of covid-19 using ensemble of transfer learning with deep convolutional. International Journal of Computer Assisted Radiology and Surgery, 16.
Gunraj, H., Wang, L., and Wong, A. (2020). Covidnet-ct: A tailored deep convolutional neural network design for detection of covid-19 cases from chest ct images. Frontiers in Medicine, 7:1025.
Hu, K., Huang, Y., Huang, W., Tan, H., Chen, Z., Zhong, Z., Li, X., Zhang, Y., and Gao, X. (2021). Deep supervised learning using self-adaptive auxiliary loss for covid-19 diagnosis from imbalanced ct images. volume 458, pages 232–245.
Islam, M. R. and Matin, A. (2020). Detection of covid 19 from ct image by the novel lenet-5 cnn architecture. In 2020 23rd ICCIT, pages 1–5.
Jia, G., Lam, H.-K., and Xu, Y. (2021). Classication of covid-19 chest x-ray and ct images using a type of dynamic cnn modication method. Computers in Biology and Medicine, 134:104425.
Júnior, E. A. and Yamashita, H. (Maio 2001). Aspectos básicos de tomografia computadorizada e ressonância magnética. Brazilian Journal of Psychiatry, 23.
Kamel, M. A., Abdelshafy, M., AbdulRazek, M., Abouelkhir, O., Fawzy, A., and Sahlol, A. T. (2021). Efficient classification approach based on covid-19 ct images analysis with deep features. In 2021 ICCCIS, pages 459–464.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Lai, Yaoming e Li, G., Wu, D., Lian, W., Li, C., Tian, J., Ma, X., Chen, H., Xu, W., Wei, J., Zhang, Y., and Jiang, G. (2020). 2019 novel coronavirus-infected pneumonia on ct: A feasibility study of few-shot learning for computerized diagnosis of emergency diseases. IEEE, 8:194158–194165.
Li, C., Yang, Y., Liang, H., and Wu, B. (2021). Transfer learning for establishment of recognition of covid-19 on ct imaging using small-sized training datasets. Knowledge-Based Systems, 218:106849.
Mahase, E. (2020). Coronavirus: covid-19 has killed more people than sars and mers combined, despite lower case fatality rate. volume 368. BMJ.
Mishra, N., Singh, P., and Joshi, S. (2021). Automated detection of covid-19 from ct scan using convolutional neural network. Biocybernetics and Biomedical Engineering, 41:572–588.
Organization, W. H. (2020). Coronavirus. World Health Organization.
Ponti, M. A. and da Costa, G. B. P. (Tópicos em Gerenciamento de Dados e Informações, 2017). Como funciona o deep learning. SBC, 1a ed., 23.
Rahimzadeh, M., Attar, A., and Sakhaei, S. M. (2021). A fully automated deep learning-based network for detecting covid-19 from a new and large lung ct scan dataset. In Biomedical Signal Processing and Control, page 102588.
Sari, I. P., Widodo, Nugraheni, M., and Wanda, P. (2020). A basic concept of image classification for covid-19 patients using chest ct scan and convolutional neural network. In 2020 1st ICITAMEE, pages 175–178.
Serte, S. and Serener, A. (2020). Discerning covid-19 from mycoplasma and viral pneumonia on ct images via deep learning. In 2020 4th ISMSIT, pages 1–5.
Seum, A., Raj, A. H., Sakib, S., and Hossain, T. (2020). A comparative study of cnn transfer learning classication algorithms with segmentation for covid-19 detection from ct scan images. In 2020 11th ICECE, pages 234–237.
Turkoglu, M. (2021). Covid-19 detection system using chest ct images and multiple kernels-extreme learning machine based on deep neural network. IRBM, 42:207–214.
Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., and Tan, W. (2020a). Detection of SARS-CoV-2 in Different Types of Clinical Specimens. pages 1843–1844. JAMA.
Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., and Zheng, C. (2020b). A weakly supervised framework for covid-19 classication and lesion localization from chest ct. IEEE Transactions on Medical Imaging, 39(8):2615–2625.
Wang, Z., Liu, Q., and Dou, Q. (2020c). Contrastive cross-site learning with redesigned net for covid-19 ct classication. IEEE J. Biomed. Health Inform., 24(10):2806–2813.
Yener, F. M. and Oktay, A. B. (2020). Diagnosis of covid-19 with a deep learning approach on chest ct slices. In 2020 TIPTEKNO, pages 1–4.
Zhang, Y.-D., Satapathy, S. C., Zhu, L.-Y., Górriz, J. M., and Wang, S.-H. (2020). A seven-layer convolutional neural network for chest ct based covid-19 diagnosis using stochastic pooling. pages 1–1. IEEE Sensors Journal.
Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., and Dong, Y. (2021). The ensemble deep learning model for novel covid-19 on ct images. Applied Soft Computing, 98:106885.
Publicado
23/11/2021
Como Citar
MARQUES, Júlio V. M.; VERAS, Rodrigo M. S.; SILVA, Romuere R. V..
A Literature Review: Detection of COVID-19 in Computed Tomography Images Using Deep Learning. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 14. , 2021, Picos.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 9-16.
DOI: https://doi.org/10.5753/enucompi.2021.17748.
