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Prediction of Covid-19 disease with Resnet-101 deep learning architecture using Computerized Tomography images

Yıl 2022, , 36 - 42, 29.06.2022
https://doi.org/10.46810/tdfd.1095624

Öz

Many pandemics have caused the deaths of millions of people in world history from past to present. Therefore, the measures to be taken in the prevention of pandemics are of great importance. In addition to the precautions, it is very important to be able to diagnose the disease early. The most recent pandemic occurred in the world is the COVID-19 outbreak that emerged in China in late 2019. In this study, Computerized Tomography images of 746 patients taken from an open source (GitHub) website were used. Images were analyzed using the Resnet-101 model, which is one of the deep learning architectures. Classification process was carried out with the created Resnet-101 model. With the Resnet-101 model, individuals with Covid-19 disease were tried to be identified. The Resnet-101 model detected individuals with Covid-19 disease with an accuracy rate of 94.29%.

Teşekkür

We would like to thank Zhao et al. who opened the data set used in the study for open access.

Kaynakça

  • Çalışkan, A. (2019). XIX. Yüzyıl ve XX. Yüzyıl başlarında Maraş ve kazalarında salgın hastalıklar ve salgın hastalıklara karşı alınan önlemler. Paradigma Akademi.
  • Benedictow, O. J., & Benedictow, O. L. (2004). The Black Death, 1346-1353: the complete history. Boydell & Brewer.
  • Condrau, F. (2020). Samuel K. Cohn Jr., Epidemics: Hate and Compassion from the Plague of Athens to AIDS. Social History of Medicine.
  • Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2020). De novo design of new chemical entities (NCEs) for SARS-CoV-2 using artificial intelligence.
  • Dikmen, A. U., KINA, M. H., Özkan, S., & İlhan, M. N. (2020). Epidemiology of COVID-19: What We Learn From Pandemic. Journal of Biotechnology and Strategic Health Research, 4, 29-36.
  • Macleod, I., & Heath, N. (2008). Cone-beam computed tomography (CBCT) in dental practice. Dental update, 35(9), 590-598.
  • Li, X., Zeng, X., Liu, B., & Yu, Y. (2020a). COVID-19 infection presenting with CT halo sign. Radiology: Cardiothoracic Imaging, 2(1), e200026. doi: 10.1148/ryct.2020200026.
  • Fang, Y., Zhang, H., Xu, Y., Xie, J., Pang, P., & Ji, W. (2020). CT manifestations of two cases of 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology, 295(1), 208-209.
  • Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020). Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing. Radiology, 200343. doi: 10.1148/radiol.2020200343.
  • Kay, F., & Abbara, S. (2020). The Many Faces of COVID-19: Spectrum of Imaging Manifestations. Radiology: Cardiothoracic Imaging, 2(1), e200037. doi: 10.1148/ryct.2020200037
  • Phelan, A. L., Katz, R., & Gostin, L. O. (2020). The novel coronavirus originating in Wuhan, China: challenges for global health governance. Jama, 323(8), 709-710. doi:10.1001/jama.2020.1097.
  • Nishiura, H., Jung, S. M., Linton, N. M., Kinoshita, R., Yang, Y., Hayashi, K., ... & Akhmetzhanov, A. R. (2020). The extent of transmission of novel coronavirus in Wuhan, China, 2020. doi: 10.3390/jcm9020330.
  • Song, F., Shi, N., Shan, F., Zhang, Z., Shen, J., Lu, H., ... & Shi, Y. (2020). Emerging 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology, 295(1), 210-217.
  • Liu, T., Huang, P., Liu, H., Huang, L., Lei, M., Xu, W., ... & Liu, B. (2020). Spectrum of chest CT findings in a familial cluster of COVID-19 infection. Radiology: Cardiothoracic Imaging, 2(1), e200025. doi: 10.1148/ryct.2020200025.
  • Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., ... & Zheng, C. (2020). Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology, 200370. doi: 10.1148/radiol.2020200370.
  • Kong, W., & Agarwal, P. P. (2020). Chest imaging appearance of COVID-19 infection. Radiology: Cardiothoracic Imaging, 2(1), e200028. doi: 10.1148/ryct.2020200028.
  • Ng, M. Y., Lee, E. Y., Yang, J., Yang, F., Li, X., Wang, H., ... & Hui, C. K. M. (2020). Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiology: Cardiothoracic Imaging, 2(1), e200034. doi: 10.1148/ryct.2020200034.
  • Wu, Y., Xie, Y. L., & Wang, X. (2020). Longitudinal CT findings in COVID-19 pneumonia: case presenting organizing pneumonia pattern. Radiology: Cardiothoracic Imaging, 2(1), e200031. doi: 10.1148/ryct.2020200031.
  • Ayan, A., Kıraç, F. S., & Ayan, (2020) U. D. A. COVID-19 Pandemisi Sürecinde Nükleer Tıp Uygulamaları İçin Kılavuz .
  • Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., ... & Cao, K. (2020b). Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology, 200905.
  • Azuaje, F. (2019). Artificial intelligence for precision oncology: beyond patient stratification. NPJ precision oncology, 3(1), 1-5.
  • Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., ... & Chau, D. H. (2020b). CNN 101: Interactive Visual Learning for Convolutional Neural Networks. arXiv preprint arXiv:2001.02004.
  • Akour, M., Al, S. H., & Al Qasem, O. (2020). The effectiveness of using deep learning algorithms in predicting students achievements. Indonesian J. Elect. Eng. Comput. Sci., 19(1), 387-393.
  • Khan, S., Javed, M. H., Ahmed, E., Shah, S. A., & Ali, S. U. (2019, March). Facial Recognition using Convolutional Neural Networks and Implementation on Smart Glasses. In 2019 International Conference on Information Science and Communication Technology (ICISCT) (pp. 1-6). IEEE.
  • De Bortoli, L., Guzzi, F., Marsi, S., Carrato, S., & Ramponi, G. (2019, September). A fast face recognition CNN obtained by distillation. In International Conference on Applications in Electronics Pervading Industry, Environment and Society (pp. 341-347). Springer, Cham.
  • Amiriparian, S., Awad, A., Gerczuk, M., Stappen, L., Baird, A., Ottl, S., & Schuller, B. (2019, July). Audio-based recognition of bipolar disorder utilising capsule networks. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
  • Chao, H., Dong, L., Liu, Y., & Lu, B. (2019). Emotion recognition from multiband EEG signals using CapsNet. Sensors, 19(9), 2212.
  • Choi, Y. H., Liu, P., Shang, Z., Wang, H., Wang, Z., Zhang, L., ... & Zou, Q. (2019). Using Deep Learning to Solve Computer Security Challenges: A Survey. arXiv preprint arXiv:1912.05721.
  • Khedkar, S., Gandhi, P., Shinde, G., & Subramanian, V. (2020). Deep Learning and Explainable AI in Healthcare Using EHR. In Deep Learning Techniques for Biomedical and Health Informatics (pp. 129-148). Springer, Cham.
  • Uddin, M. Z. (2019). A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system. Journal of Parallel and Distributed Computing, 123, 46-53.
  • Github (2020) UCSD-AI4H/COVID-CT. (2020). Retrieved 12 May 2020, from https://github.com/UCSD-AI4H /COVID-CT
  • Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Alashqar, B. A. (2020). COVID 19 Detection using Artificial Intelligence.
  • Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 1.
  • Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., ... & Xu, B. (2020a). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv.
  • Wang, L., & Wong, A. (2020). COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv preprint arXiv:2003.09871.
  • Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., ... & Shi, Y. (2020). Lung infection quantification of covid-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655.
  • Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
  • Zhao, J., Zhang, Y., He, X., & Xie, P. (2020). COVID-CT-Dataset: a CT scan dataset about COVID 19. arXiv preprint arXiv:2003.13865.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Chung, Y. M., Hu, C. S., Lawson, A., & Smyth, C. (2019). TopoResNet: A hybrid deep learning architecture and its application to skin lesion classification. arXiv preprint arXiv:1905.08607.
  • Koç, M., & Özdemir, R. (2019). Enhancing Facial Expression Recognition in the Wild with Deep Learning Methods Using a New Dataset: RidNet. Bilecik Seyh Edebali University Journal of Science, 6(2). doi: 10.35193/bseufbd.645138
  • Šimundić, A. M. (2009). Measures of diagnostic accuracy: basic definitions. Ejifcc, 19(4), 203.
  • Zhu, W., Zeng, N., & Wang, N. (2010). Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG proceedings: health care and life sciences, Baltimore, Maryland, 19, 67.
  • Lalkhen, A. G., & McCluskey, A. (2008). Clinical tests: sensitivity and specificity. Continuing Education in Anaesthesia Critical Care & Pain, 8(6), 221-223.
  • Eusebi, P. (2013). Diagnostic accuracy measures. Cerebrovascular Diseases, 36(4), 267-272.
  • Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1), 6.

Resnet-101 Derin Öğrenme Mimarisi ile Bilgisayarlı Tomografi Görüntüleri Kullanılarak Covid-19 Hastalığının Tahminlenmesi

Yıl 2022, , 36 - 42, 29.06.2022
https://doi.org/10.46810/tdfd.1095624

Öz

Geçmişten günümüze dünya tarihinde birçok salgın milyonlarca insanın ölümüne neden olmuştur. Bu nedenle salgınlardan korunmada alınacak önlemler büyük önem taşımaktadır. Önlemlere ek olarak, hastalığın erken teşhis edilebilmesi çok önemlidir. Dünyada en son yaşanan salgın 2019 yılının sonlarında Çin'de ortaya çıkan COVID-19 salgınıdır. Bu çalışmada 746 hastanın açık kaynaklı (GitHub) bir web sitesinden alınan Bilgisayarlı Tomografi görüntüleri kullanılmıştır. Görüntüler, derin öğrenme mimarilerinden Resnet-101 modeli kullanılarak analiz edilmiştir. Oluşturulan Resnet-101 modeli ile sınıflandırma işlemi gerçekleştirilmiştir. Resnet- 101 modeli ile Covid-19 hastalığı olan bireyler tespit edilmeye çalışılmıştır. Resnet-101 modeli, Covid-19 hastalığı olan bireyleri %94,29 doğruluk oranıyla tespit ettiği belirlenmiştir.

Kaynakça

  • Çalışkan, A. (2019). XIX. Yüzyıl ve XX. Yüzyıl başlarında Maraş ve kazalarında salgın hastalıklar ve salgın hastalıklara karşı alınan önlemler. Paradigma Akademi.
  • Benedictow, O. J., & Benedictow, O. L. (2004). The Black Death, 1346-1353: the complete history. Boydell & Brewer.
  • Condrau, F. (2020). Samuel K. Cohn Jr., Epidemics: Hate and Compassion from the Plague of Athens to AIDS. Social History of Medicine.
  • Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2020). De novo design of new chemical entities (NCEs) for SARS-CoV-2 using artificial intelligence.
  • Dikmen, A. U., KINA, M. H., Özkan, S., & İlhan, M. N. (2020). Epidemiology of COVID-19: What We Learn From Pandemic. Journal of Biotechnology and Strategic Health Research, 4, 29-36.
  • Macleod, I., & Heath, N. (2008). Cone-beam computed tomography (CBCT) in dental practice. Dental update, 35(9), 590-598.
  • Li, X., Zeng, X., Liu, B., & Yu, Y. (2020a). COVID-19 infection presenting with CT halo sign. Radiology: Cardiothoracic Imaging, 2(1), e200026. doi: 10.1148/ryct.2020200026.
  • Fang, Y., Zhang, H., Xu, Y., Xie, J., Pang, P., & Ji, W. (2020). CT manifestations of two cases of 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology, 295(1), 208-209.
  • Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020). Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing. Radiology, 200343. doi: 10.1148/radiol.2020200343.
  • Kay, F., & Abbara, S. (2020). The Many Faces of COVID-19: Spectrum of Imaging Manifestations. Radiology: Cardiothoracic Imaging, 2(1), e200037. doi: 10.1148/ryct.2020200037
  • Phelan, A. L., Katz, R., & Gostin, L. O. (2020). The novel coronavirus originating in Wuhan, China: challenges for global health governance. Jama, 323(8), 709-710. doi:10.1001/jama.2020.1097.
  • Nishiura, H., Jung, S. M., Linton, N. M., Kinoshita, R., Yang, Y., Hayashi, K., ... & Akhmetzhanov, A. R. (2020). The extent of transmission of novel coronavirus in Wuhan, China, 2020. doi: 10.3390/jcm9020330.
  • Song, F., Shi, N., Shan, F., Zhang, Z., Shen, J., Lu, H., ... & Shi, Y. (2020). Emerging 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology, 295(1), 210-217.
  • Liu, T., Huang, P., Liu, H., Huang, L., Lei, M., Xu, W., ... & Liu, B. (2020). Spectrum of chest CT findings in a familial cluster of COVID-19 infection. Radiology: Cardiothoracic Imaging, 2(1), e200025. doi: 10.1148/ryct.2020200025.
  • Pan, F., Ye, T., Sun, P., Gui, S., Liang, B., Li, L., ... & Zheng, C. (2020). Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology, 200370. doi: 10.1148/radiol.2020200370.
  • Kong, W., & Agarwal, P. P. (2020). Chest imaging appearance of COVID-19 infection. Radiology: Cardiothoracic Imaging, 2(1), e200028. doi: 10.1148/ryct.2020200028.
  • Ng, M. Y., Lee, E. Y., Yang, J., Yang, F., Li, X., Wang, H., ... & Hui, C. K. M. (2020). Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiology: Cardiothoracic Imaging, 2(1), e200034. doi: 10.1148/ryct.2020200034.
  • Wu, Y., Xie, Y. L., & Wang, X. (2020). Longitudinal CT findings in COVID-19 pneumonia: case presenting organizing pneumonia pattern. Radiology: Cardiothoracic Imaging, 2(1), e200031. doi: 10.1148/ryct.2020200031.
  • Ayan, A., Kıraç, F. S., & Ayan, (2020) U. D. A. COVID-19 Pandemisi Sürecinde Nükleer Tıp Uygulamaları İçin Kılavuz .
  • Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., ... & Cao, K. (2020b). Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology, 200905.
  • Azuaje, F. (2019). Artificial intelligence for precision oncology: beyond patient stratification. NPJ precision oncology, 3(1), 1-5.
  • Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., ... & Chau, D. H. (2020b). CNN 101: Interactive Visual Learning for Convolutional Neural Networks. arXiv preprint arXiv:2001.02004.
  • Akour, M., Al, S. H., & Al Qasem, O. (2020). The effectiveness of using deep learning algorithms in predicting students achievements. Indonesian J. Elect. Eng. Comput. Sci., 19(1), 387-393.
  • Khan, S., Javed, M. H., Ahmed, E., Shah, S. A., & Ali, S. U. (2019, March). Facial Recognition using Convolutional Neural Networks and Implementation on Smart Glasses. In 2019 International Conference on Information Science and Communication Technology (ICISCT) (pp. 1-6). IEEE.
  • De Bortoli, L., Guzzi, F., Marsi, S., Carrato, S., & Ramponi, G. (2019, September). A fast face recognition CNN obtained by distillation. In International Conference on Applications in Electronics Pervading Industry, Environment and Society (pp. 341-347). Springer, Cham.
  • Amiriparian, S., Awad, A., Gerczuk, M., Stappen, L., Baird, A., Ottl, S., & Schuller, B. (2019, July). Audio-based recognition of bipolar disorder utilising capsule networks. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
  • Chao, H., Dong, L., Liu, Y., & Lu, B. (2019). Emotion recognition from multiband EEG signals using CapsNet. Sensors, 19(9), 2212.
  • Choi, Y. H., Liu, P., Shang, Z., Wang, H., Wang, Z., Zhang, L., ... & Zou, Q. (2019). Using Deep Learning to Solve Computer Security Challenges: A Survey. arXiv preprint arXiv:1912.05721.
  • Khedkar, S., Gandhi, P., Shinde, G., & Subramanian, V. (2020). Deep Learning and Explainable AI in Healthcare Using EHR. In Deep Learning Techniques for Biomedical and Health Informatics (pp. 129-148). Springer, Cham.
  • Uddin, M. Z. (2019). A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system. Journal of Parallel and Distributed Computing, 123, 46-53.
  • Github (2020) UCSD-AI4H/COVID-CT. (2020). Retrieved 12 May 2020, from https://github.com/UCSD-AI4H /COVID-CT
  • Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Alashqar, B. A. (2020). COVID 19 Detection using Artificial Intelligence.
  • Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 1.
  • Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., ... & Xu, B. (2020a). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv.
  • Wang, L., & Wong, A. (2020). COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv preprint arXiv:2003.09871.
  • Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., ... & Shi, Y. (2020). Lung infection quantification of covid-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655.
  • Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
  • Zhao, J., Zhang, Y., He, X., & Xie, P. (2020). COVID-CT-Dataset: a CT scan dataset about COVID 19. arXiv preprint arXiv:2003.13865.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Chung, Y. M., Hu, C. S., Lawson, A., & Smyth, C. (2019). TopoResNet: A hybrid deep learning architecture and its application to skin lesion classification. arXiv preprint arXiv:1905.08607.
  • Koç, M., & Özdemir, R. (2019). Enhancing Facial Expression Recognition in the Wild with Deep Learning Methods Using a New Dataset: RidNet. Bilecik Seyh Edebali University Journal of Science, 6(2). doi: 10.35193/bseufbd.645138
  • Šimundić, A. M. (2009). Measures of diagnostic accuracy: basic definitions. Ejifcc, 19(4), 203.
  • Zhu, W., Zeng, N., & Wang, N. (2010). Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG proceedings: health care and life sciences, Baltimore, Maryland, 19, 67.
  • Lalkhen, A. G., & McCluskey, A. (2008). Clinical tests: sensitivity and specificity. Continuing Education in Anaesthesia Critical Care & Pain, 8(6), 221-223.
  • Eusebi, P. (2013). Diagnostic accuracy measures. Cerebrovascular Diseases, 36(4), 267-272.
  • Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1), 6.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Bekir Aksoy 0000-0001-8052-9411

Osamah Khaled Musleh Salman 0000-0001-6526-4793

Yayımlanma Tarihi 29 Haziran 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Aksoy, B., & Salman, O. K. M. (2022). Prediction of Covid-19 disease with Resnet-101 deep learning architecture using Computerized Tomography images. Türk Doğa Ve Fen Dergisi, 11(2), 36-42. https://doi.org/10.46810/tdfd.1095624
AMA Aksoy B, Salman OKM. Prediction of Covid-19 disease with Resnet-101 deep learning architecture using Computerized Tomography images. TDFD. Haziran 2022;11(2):36-42. doi:10.46810/tdfd.1095624
Chicago Aksoy, Bekir, ve Osamah Khaled Musleh Salman. “Prediction of Covid-19 Disease With Resnet-101 Deep Learning Architecture Using Computerized Tomography Images”. Türk Doğa Ve Fen Dergisi 11, sy. 2 (Haziran 2022): 36-42. https://doi.org/10.46810/tdfd.1095624.
EndNote Aksoy B, Salman OKM (01 Haziran 2022) Prediction of Covid-19 disease with Resnet-101 deep learning architecture using Computerized Tomography images. Türk Doğa ve Fen Dergisi 11 2 36–42.
IEEE B. Aksoy ve O. K. M. Salman, “Prediction of Covid-19 disease with Resnet-101 deep learning architecture using Computerized Tomography images”, TDFD, c. 11, sy. 2, ss. 36–42, 2022, doi: 10.46810/tdfd.1095624.
ISNAD Aksoy, Bekir - Salman, Osamah Khaled Musleh. “Prediction of Covid-19 Disease With Resnet-101 Deep Learning Architecture Using Computerized Tomography Images”. Türk Doğa ve Fen Dergisi 11/2 (Haziran 2022), 36-42. https://doi.org/10.46810/tdfd.1095624.
JAMA Aksoy B, Salman OKM. Prediction of Covid-19 disease with Resnet-101 deep learning architecture using Computerized Tomography images. TDFD. 2022;11:36–42.
MLA Aksoy, Bekir ve Osamah Khaled Musleh Salman. “Prediction of Covid-19 Disease With Resnet-101 Deep Learning Architecture Using Computerized Tomography Images”. Türk Doğa Ve Fen Dergisi, c. 11, sy. 2, 2022, ss. 36-42, doi:10.46810/tdfd.1095624.
Vancouver Aksoy B, Salman OKM. Prediction of Covid-19 disease with Resnet-101 deep learning architecture using Computerized Tomography images. TDFD. 2022;11(2):36-42.