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Yıl 2021, Cilt: 2 Sayı: 2, 56 - 68, 15.12.2021

Ebru Erdem Tolga Aydin

Öz

Kaynakça

  • [1] World Health Organization, https://www.who.int/emergencies/diseases/novel-coronavirus-2019, (accessed 28 September 2020).
  • [2] worldometer, https://www.worldometers.info/coronavirus/, (accessed 28 September 2020).
  • [3] Cinkooglu A., Esmat H. A., Recep S., & Forogh M. N. (2020). “COVID-19 presenting with a small ground-glass opacity in the upper lobe of the lung”. Eurorad.
  • [4] Parekh, M., Donuru, A., Balasubramanya, R., & Kapur, S. (2020). “Review of the chest CT differential diagnosis of ground-glass opacities in the COVID era”. Radiology, 202504.
  • [5] Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Soufi, G. J. (2020). “Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning”. arXiv preprint arXiv:2004.09363.
  • [6] Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., Bhardwaj, P., & Singh, V. (2020). “A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images”. Chaos, Solitons & Fractals, 140, 110190.
  • [7] Ismael, A. M., & Şengür, A. (2020). “Deep learning approaches for COVID-19 detection based on chest X-ray images”. Expert Systems with Applications, 164, 114054.
  • [8] Jain, G., Mittal, D., Thakur, D., & Mittal, M. K. (2020). “A deep learning approach to detect Covid-19 coronavirus with X-Ray images”. Biocybernetics and Biomedical Engineering, 40(4), 1391-1405.
  • [9] Ouchicha, C., Ammor, O., & Meknassi, M. (2020). “CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images”. Chaos, Solitons & Fractals, 140, 110245.
  • [10] Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., ... & Xia, L. (2020). “Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases”. Radiology, 200642.
  • [11] Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). “Sensitivity of chest CT for COVID-19: comparison to RT-PCR”. Radiology, 200432.
  • [12] Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & 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, 103795.
  • [13] Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., ... & Ye, L. (2020). “Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography”. Cell.
  • [14] Attallah, O., Ragab, D. A., & Sharkas, M. (2020). “MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks”. PeerJ, 8, e10086.
  • [15] Yasar, H., & Ceylan, M. (2020). “A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods”. Multimedia Tools and Applications, 1-25.
  • [16] Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., ... & Blain, M. (2020). “Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets”. Nature communications, 11(1), 1-7.
  • [17] Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020). “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning”. Journal of Biomolecular Structure and Dynamics, 1-8.
  • [18] Li, Z., Zhong, Z., Li, Y., Zhang, T., Gao, L., Jin, D., ... & Xiao, J. (2020). “From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans”. medRxiv.
  • [19] Hasan, A. M., AL-Jawad, M. M., Jalab, H. A., Shaiba, H., Ibrahim, R. W., & AL-Shamasneh, A. A. R. (2020). “Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features”. Entropy, 22(5), 517.
  • [20] Ko, H., Chung, H., Kang, W. S., Kim, K. W., Shin, Y., Kang, S. J., ... & Lee, J. (2020). “COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT Image: Model Development and Validation”. Journal of Medical Internet Research, 22(6), e19569.
  • [21] Ahuja, S., Panigrahi, B. K., Dey, N., Rajinikanth, V., & Gandhi, T. K. (2020). “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices”.
  • [22] Dansana, D., Kumar, R., Bhattacharjee, A., Hemanth, D. J., Gupta, D., Khanna, A., & Castillo, O. (2020). “Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm”. Soft Computing, 1-9.
  • [23] Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., & Singh, V. (2020). “Application of Deep Learning for Fast Detection of COVID-19 in X-Rays using nCOVnet”. Chaos, Solitons & Fractals, 109944.
  • [24] Karar, M. E., Hemdan, E. E. D., & Shouman, M. A. (2020). “Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans”. Complex & Intelligent Systems, 1-13.
  • [25] Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., ... & Zhao, X. (2020). “Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches”. Journal of X-ray Science and Technology, (Preprint), 1-19.
  • [26] Elasnaoui, K., & Chawki, Y. (2020). “Using X-ray images and deep learning for automated detection of coronavirus disease”. Journal of Biomolecular Structure and Dynamics, (just-accepted), 1-22.
  • [27] China National Center for Bioinformation 2019 Novel Coronavirus Resource (2019nCoVR), http://ncov-ai.big.ac.cn/download?lang=en, (accessed 12 October 2020).
  • [28] Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., & Feng, J. (2017). “Dual path networks”. In Advances in neural information processing systems (pp. 4467-4475).
  • [29] Singh, K. K., Siddhartha, M., & Singh, A. (2020). “Diagnosis of Coronavirus Disease (COVID-19) from Chest X-ray images using modified XceptionNet”. Romanian Journal of Information Science and Technology, 23, S91-105.
  • [30] Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). “Inception-v4, inception-ResNet and the impact of residual connections on learning”. 31st AAAI. In Conf Artif Intell AAAI 2017.
  • [31] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). “Mobilenets: Efficient convolutional neural networks for mobile vision applications”. arXiv preprint arXiv:1704.04861.
  • [32] Szegedy, C., Ioffe, S., & Vanhoucke, V. et Alemi, AA (2017). “In Inception-v4, inception-resnet and the impact of residual connections on learning”. In Thirty-First AAAI Conference on Artificial Intelligence.
  • [33] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). “Going deeper with convolutions”. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • [34] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). “Rethinking the inception architecture for computer vision”. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
  • [35] Simonyan, K., & Zisserman, A. (2014). “Very deep convolutional networks for large-scale image recognition”. arXiv preprint arXiv:1409.1556.
  • [36] Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size”. arXiv preprint arXiv:1602.07360.
  • [37] Başaran, E., Cömert, Z., & Çelik, Y. (2020). “Convolutional neural network approach for automatic tympanic membrane detection and classification”. Biomedical Signal Processing and Control, 56, 101734.
  • [38] Xu, C., Yang, J., Lai, H., Gao, J., Shen, L., & Yan, S. (2019). “UP-CNN: Un-pooling augmented convolutional neural network”. Pattern Recognition Letters, 119, 34-40.
  • [39] 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). [40] F. Chollet, “Keras”, 2015.

Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach

Yıl 2021, Cilt: 2 Sayı: 2, 56 - 68, 15.12.2021

Ebru Erdem Tolga Aydin

Öz

The COVID-19 has become a pressing public health concern recently due to its dramatic impact. It spreads quickly, and it is beyond the ability of health staff to detect patients with the disease immediately. However, the ability to diagnose SARS-CoV-2 in a short time is critical for fighting the disease. The primary objective of this study is to develop deep neural networks to diagnose disease in a quick, safe, and cheap way. We classify the cases as normal, COVID-19, and pneumonia. Deep neural networks are developed to perform a three-class classification task. Ten deep learning models are evaluated on a large dataset. Although all DCNNs demonstrated promising potential for classification, hybrid neural networks delivered the most promising outcome with the highest accuracies. The first hybrid model is named MICOVID. The second hybrid model is named VVCOVID. These models are developed through transfer learning by using pre-trained deep learning models. Performance metrics results showed that MICOVID and VVCOVID models have an accuracy of 94% for COVID-19 detection. This is higher than other classification models. These findings suggest that two novel hybrid models that we proposed have great potential to be embedded into computer-aided systems to predict disease in radiology departments.

Anahtar Kelimeler

COVID-19 deep neural network hybrid models CNN

Kaynakça

  • [1] World Health Organization, https://www.who.int/emergencies/diseases/novel-coronavirus-2019, (accessed 28 September 2020).
  • [2] worldometer, https://www.worldometers.info/coronavirus/, (accessed 28 September 2020).
  • [3] Cinkooglu A., Esmat H. A., Recep S., & Forogh M. N. (2020). “COVID-19 presenting with a small ground-glass opacity in the upper lobe of the lung”. Eurorad.
  • [4] Parekh, M., Donuru, A., Balasubramanya, R., & Kapur, S. (2020). “Review of the chest CT differential diagnosis of ground-glass opacities in the COVID era”. Radiology, 202504.
  • [5] Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Soufi, G. J. (2020). “Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning”. arXiv preprint arXiv:2004.09363.
  • [6] Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., Bhardwaj, P., & Singh, V. (2020). “A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images”. Chaos, Solitons & Fractals, 140, 110190.
  • [7] Ismael, A. M., & Şengür, A. (2020). “Deep learning approaches for COVID-19 detection based on chest X-ray images”. Expert Systems with Applications, 164, 114054.
  • [8] Jain, G., Mittal, D., Thakur, D., & Mittal, M. K. (2020). “A deep learning approach to detect Covid-19 coronavirus with X-Ray images”. Biocybernetics and Biomedical Engineering, 40(4), 1391-1405.
  • [9] Ouchicha, C., Ammor, O., & Meknassi, M. (2020). “CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images”. Chaos, Solitons & Fractals, 140, 110245.
  • [10] Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., ... & Xia, L. (2020). “Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases”. Radiology, 200642.
  • [11] Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). “Sensitivity of chest CT for COVID-19: comparison to RT-PCR”. Radiology, 200432.
  • [12] Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & 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, 103795.
  • [13] Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., ... & Ye, L. (2020). “Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography”. Cell.
  • [14] Attallah, O., Ragab, D. A., & Sharkas, M. (2020). “MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks”. PeerJ, 8, e10086.
  • [15] Yasar, H., & Ceylan, M. (2020). “A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods”. Multimedia Tools and Applications, 1-25.
  • [16] Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., ... & Blain, M. (2020). “Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets”. Nature communications, 11(1), 1-7.
  • [17] Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V., & Kaur, M. (2020). “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning”. Journal of Biomolecular Structure and Dynamics, 1-8.
  • [18] Li, Z., Zhong, Z., Li, Y., Zhang, T., Gao, L., Jin, D., ... & Xiao, J. (2020). “From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans”. medRxiv.
  • [19] Hasan, A. M., AL-Jawad, M. M., Jalab, H. A., Shaiba, H., Ibrahim, R. W., & AL-Shamasneh, A. A. R. (2020). “Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features”. Entropy, 22(5), 517.
  • [20] Ko, H., Chung, H., Kang, W. S., Kim, K. W., Shin, Y., Kang, S. J., ... & Lee, J. (2020). “COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT Image: Model Development and Validation”. Journal of Medical Internet Research, 22(6), e19569.
  • [21] Ahuja, S., Panigrahi, B. K., Dey, N., Rajinikanth, V., & Gandhi, T. K. (2020). “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices”.
  • [22] Dansana, D., Kumar, R., Bhattacharjee, A., Hemanth, D. J., Gupta, D., Khanna, A., & Castillo, O. (2020). “Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm”. Soft Computing, 1-9.
  • [23] Panwar, H., Gupta, P. K., Siddiqui, M. K., Morales-Menendez, R., & Singh, V. (2020). “Application of Deep Learning for Fast Detection of COVID-19 in X-Rays using nCOVnet”. Chaos, Solitons & Fractals, 109944.
  • [24] Karar, M. E., Hemdan, E. E. D., & Shouman, M. A. (2020). “Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans”. Complex & Intelligent Systems, 1-13.
  • [25] Rahaman, M. M., Li, C., Yao, Y., Kulwa, F., Rahman, M. A., Wang, Q., ... & Zhao, X. (2020). “Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches”. Journal of X-ray Science and Technology, (Preprint), 1-19.
  • [26] Elasnaoui, K., & Chawki, Y. (2020). “Using X-ray images and deep learning for automated detection of coronavirus disease”. Journal of Biomolecular Structure and Dynamics, (just-accepted), 1-22.
  • [27] China National Center for Bioinformation 2019 Novel Coronavirus Resource (2019nCoVR), http://ncov-ai.big.ac.cn/download?lang=en, (accessed 12 October 2020).
  • [28] Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., & Feng, J. (2017). “Dual path networks”. In Advances in neural information processing systems (pp. 4467-4475).
  • [29] Singh, K. K., Siddhartha, M., & Singh, A. (2020). “Diagnosis of Coronavirus Disease (COVID-19) from Chest X-ray images using modified XceptionNet”. Romanian Journal of Information Science and Technology, 23, S91-105.
  • [30] Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). “Inception-v4, inception-ResNet and the impact of residual connections on learning”. 31st AAAI. In Conf Artif Intell AAAI 2017.
  • [31] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). “Mobilenets: Efficient convolutional neural networks for mobile vision applications”. arXiv preprint arXiv:1704.04861.
  • [32] Szegedy, C., Ioffe, S., & Vanhoucke, V. et Alemi, AA (2017). “In Inception-v4, inception-resnet and the impact of residual connections on learning”. In Thirty-First AAAI Conference on Artificial Intelligence.
  • [33] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). “Going deeper with convolutions”. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • [34] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). “Rethinking the inception architecture for computer vision”. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
  • [35] Simonyan, K., & Zisserman, A. (2014). “Very deep convolutional networks for large-scale image recognition”. arXiv preprint arXiv:1409.1556.
  • [36] Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size”. arXiv preprint arXiv:1602.07360.
  • [37] Başaran, E., Cömert, Z., & Çelik, Y. (2020). “Convolutional neural network approach for automatic tympanic membrane detection and classification”. Biomedical Signal Processing and Control, 56, 101734.
  • [38] Xu, C., Yang, J., Lai, H., Gao, J., Shen, L., & Yan, S. (2019). “UP-CNN: Un-pooling augmented convolutional neural network”. Pattern Recognition Letters, 119, 34-40.
  • [39] 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). [40] F. Chollet, “Keras”, 2015.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Research Articles
Yazarlar

Ebru Erdem 0000-0002-4042-7549

Tolga Aydin 0000-0002-8971-3255

Yayımlanma Tarihi 15 Aralık 2021
Gönderilme Tarihi 8 Mayıs 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 2 Sayı: 2

Kaynak Göster

APA Erdem, E., & Aydin, T. (2021). Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach. Journal of Soft Computing and Artificial Intelligence, 2(2), 56-68.
AMA Erdem E, Aydin T. Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach. JSCAI. Aralık 2021;2(2):56-68.
Chicago Erdem, Ebru, ve Tolga Aydin. “Deep Hybrid Models for CT Images to Detect COVID-19: A Comparison of Transfer Learning Approach”. Journal of Soft Computing and Artificial Intelligence 2, sy. 2 (Aralık 2021): 56-68.
EndNote Erdem E, Aydin T (01 Aralık 2021) Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach. Journal of Soft Computing and Artificial Intelligence 2 2 56–68.
IEEE E. Erdem ve T. Aydin, “Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach”, JSCAI, c. 2, sy. 2, ss. 56–68, 2021.
ISNAD Erdem, Ebru - Aydin, Tolga. “Deep Hybrid Models for CT Images to Detect COVID-19: A Comparison of Transfer Learning Approach”. Journal of Soft Computing and Artificial Intelligence 2/2 (Aralık 2021), 56-68.
JAMA Erdem E, Aydin T. Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach. JSCAI. 2021;2:56–68.
MLA Erdem, Ebru ve Tolga Aydin. “Deep Hybrid Models for CT Images to Detect COVID-19: A Comparison of Transfer Learning Approach”. Journal of Soft Computing and Artificial Intelligence, c. 2, sy. 2, 2021, ss. 56-68.
Vancouver Erdem E, Aydin T. Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach. JSCAI. 2021;2(2):56-68.

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