Araştırma Makalesi
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Yıl 2023, Cilt: 7 Sayı: 2, 53 - 60, 19.12.2023

Öz

Kaynakça

  • [1] H. P. Chan, L. M. Hadjiiski, and R. K. Samala, “Computer‐aided diagnosis in the era of deep learning,” Medical Physics, 47(5), pp. e218-e227, 2020.
  • [2] N. Petrick, B. Sahiner, S.G. Armato III, A. Bert, L. Correale, S. Delsanto, M.T. Freedman, D. Fryd, D. Gur, L. Hadjiiski, Z. Huo, Y. Jiang, L. Morra, S. Paquerault, V. Raykar, F. Samuelson, R.M. Summers, G. Tourassi, H. Yoshida, B. Zheng, C. Zhou, and H. P. Chan, “Evaluation of Computer-Aided Detection and Diagnosis Systems,” Medical Physics, 40(8), 2013.
  • [3] K. Chockley and E. Emanuel, “The end of radiology? Three threats to the future practice of radiology,” Journal of the American College of Radiology: JACR, 13(12), pp. 1415-1420, 2016.
  • [4] A. Panthakkan, S. M. Anzar., S. Al-Mansoori, and H. Al-Ahmad, “A novel DeepNet model for the efficient detection of COVID-19 for symptomatic patients,” Biomedical Signal Processing and Control, 68, pp. 1-10, 2021.
  • [5] M.C. Younis, “Evaluation of deep learning approaches for identification of different Corona-Virus species and time series prediction,” Computerized Medical Imaging and Graphics, 90, pp. 1-13, 2021.
  • [6] E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT Scans for SARS-CoV-2 identification,” MedRxiv, 2020.
  • [7] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proc. IEEE, 1998, 86(11), p. 2278.
  • [8] (2023) Dive into Deep Learning website. [Online]. Available: https://d2l.ai/chapter_convolutional-neural-networks/lenet.html
  • [9] G. Hong, X. Chen, J. Chen, M. Zhang, Y. Ren, and X. Zhang, “A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19,” Scientific Reports, 11(1), pp. 1-13, 2021.
  • [10] E.D. Carvalho, E.D. Carvalho, A.O. de Carvalho Filho, F.H.D. De Araújo, and R.D.A.L. Rabêlo, “Diagnosis of COVID-19 in CT image using CNN and XGBoost,” in Proc. IEEE Symposium on Computers and Communications (ISCC), 2020.
  • [11] M. R. Islam and A. Matin, “Detection of COVID 19 from CT image by the novel LeNet-5 CNN architecture,” in Proc. 23rd International Conference on Computer and Information Technology (ICCIT), 2020.
  • [12] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. KDD '16: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, p. 785.
  • [13] F. Chollet, Deep Learning with Python, 2nd ed., Manning Publications, 2021.
  • [14] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. 3rd International Conference on Learning Representations (ICLR), 2015.
  • [15] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” ArXiv:1704.04861, 2017.
  • [16] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, 115(3), pp. 211-252, 2015.
  • [17] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, 2nd ed., Springer, 2021.
  • [18] J., Sun, X., Li, C., Tang, S. H., Wang, and Y. D. Zhang, “MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via Chest X-ray images,” Knowledge-Based Systems, 232, pp. 1-21, 2021.
  • [19] A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd ed., O’Reilly Media, Inc., 2019.
  • [20] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors,” ArXiv:1207.0580, 2012.
  • [21] W. Zhu, W. Yeh, J. Chen, D. Chen, A. Li, and Y. Lin, “Evolutionary convolutional neural networks using ABC,” in Proc. 11th International Conference on Machine Learning and Computing (ICMLC), 2019, p. 156.
  • [22] M.T. Dang, A Survey on Transfer Learning for COVID-19 Medical Imaging Diagnosis. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (Eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies. Springer, 2021, vol 212.
  • [23] A. Halder and B. Datta, “COVID-19 detection from lung Ct-Scan images using transfer learning approach,” Machine Learning: Science and Technology, 2(4), 2021.
  • [24] K. S. Briskline, D. Murugan, and A. Petchiammal, “COVIDnet: An efficient deep learning model for COVID-19 diagnosis on chest CT images,” International Journal of Advanced Computer Science and Applications, 13(11), pp. 832-839, 2022.
  • [25] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D.,Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” International Journal of Computer Vision, 128 (2), pp. 336-359, 2019.
  • [26] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in Proc. IEEE International Conference on Computer Vision, 2017, pp. 2961.

COVID-19 and Non-COVID-19 Classification from Lung CT-Scan Images Using Deep Convolutional Neural Networks

Yıl 2023, Cilt: 7 Sayı: 2, 53 - 60, 19.12.2023

Öz

In this study, three different convolutional neural network (CNN) architectures have been used for SARS-COV-2 infection (COVID-19) detection from lung Computerized Tomography (CT) scan images. The dataset comprises 2481 lung CT-scan images, of which 1252 are positive for COVID-19 infection. First, a simple CNN, LeNet-5, was trained from scratch, which resulted in poor classification performance with an accuracy value of 0.78. Then, to overcome the drawback of the limited availability of data, the convolutional bases of two pre-trained networks, VGG-16 and MobileNet, were leveraged to extract features from the dataset. On top of the feature extraction outputs, new classifiers were trained. When the VGG16 and the MobileNet CNN’s convolutional bases were used for feature extraction, accuracy values of 0.974 and 0.984 were obtained, respectively. The findings indicate that using pre-trained CNN models for feature extraction and then training a simpler, fully connected network structure for classification successfully differentiates CT-scan images of patients with COVID-19 infection from the ones without COVID-19 infection.

Kaynakça

  • [1] H. P. Chan, L. M. Hadjiiski, and R. K. Samala, “Computer‐aided diagnosis in the era of deep learning,” Medical Physics, 47(5), pp. e218-e227, 2020.
  • [2] N. Petrick, B. Sahiner, S.G. Armato III, A. Bert, L. Correale, S. Delsanto, M.T. Freedman, D. Fryd, D. Gur, L. Hadjiiski, Z. Huo, Y. Jiang, L. Morra, S. Paquerault, V. Raykar, F. Samuelson, R.M. Summers, G. Tourassi, H. Yoshida, B. Zheng, C. Zhou, and H. P. Chan, “Evaluation of Computer-Aided Detection and Diagnosis Systems,” Medical Physics, 40(8), 2013.
  • [3] K. Chockley and E. Emanuel, “The end of radiology? Three threats to the future practice of radiology,” Journal of the American College of Radiology: JACR, 13(12), pp. 1415-1420, 2016.
  • [4] A. Panthakkan, S. M. Anzar., S. Al-Mansoori, and H. Al-Ahmad, “A novel DeepNet model for the efficient detection of COVID-19 for symptomatic patients,” Biomedical Signal Processing and Control, 68, pp. 1-10, 2021.
  • [5] M.C. Younis, “Evaluation of deep learning approaches for identification of different Corona-Virus species and time series prediction,” Computerized Medical Imaging and Graphics, 90, pp. 1-13, 2021.
  • [6] E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT Scans for SARS-CoV-2 identification,” MedRxiv, 2020.
  • [7] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proc. IEEE, 1998, 86(11), p. 2278.
  • [8] (2023) Dive into Deep Learning website. [Online]. Available: https://d2l.ai/chapter_convolutional-neural-networks/lenet.html
  • [9] G. Hong, X. Chen, J. Chen, M. Zhang, Y. Ren, and X. Zhang, “A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19,” Scientific Reports, 11(1), pp. 1-13, 2021.
  • [10] E.D. Carvalho, E.D. Carvalho, A.O. de Carvalho Filho, F.H.D. De Araújo, and R.D.A.L. Rabêlo, “Diagnosis of COVID-19 in CT image using CNN and XGBoost,” in Proc. IEEE Symposium on Computers and Communications (ISCC), 2020.
  • [11] M. R. Islam and A. Matin, “Detection of COVID 19 from CT image by the novel LeNet-5 CNN architecture,” in Proc. 23rd International Conference on Computer and Information Technology (ICCIT), 2020.
  • [12] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. KDD '16: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, p. 785.
  • [13] F. Chollet, Deep Learning with Python, 2nd ed., Manning Publications, 2021.
  • [14] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. 3rd International Conference on Learning Representations (ICLR), 2015.
  • [15] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” ArXiv:1704.04861, 2017.
  • [16] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, 115(3), pp. 211-252, 2015.
  • [17] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, 2nd ed., Springer, 2021.
  • [18] J., Sun, X., Li, C., Tang, S. H., Wang, and Y. D. Zhang, “MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via Chest X-ray images,” Knowledge-Based Systems, 232, pp. 1-21, 2021.
  • [19] A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd ed., O’Reilly Media, Inc., 2019.
  • [20] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors,” ArXiv:1207.0580, 2012.
  • [21] W. Zhu, W. Yeh, J. Chen, D. Chen, A. Li, and Y. Lin, “Evolutionary convolutional neural networks using ABC,” in Proc. 11th International Conference on Machine Learning and Computing (ICMLC), 2019, p. 156.
  • [22] M.T. Dang, A Survey on Transfer Learning for COVID-19 Medical Imaging Diagnosis. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (Eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies. Springer, 2021, vol 212.
  • [23] A. Halder and B. Datta, “COVID-19 detection from lung Ct-Scan images using transfer learning approach,” Machine Learning: Science and Technology, 2(4), 2021.
  • [24] K. S. Briskline, D. Murugan, and A. Petchiammal, “COVIDnet: An efficient deep learning model for COVID-19 diagnosis on chest CT images,” International Journal of Advanced Computer Science and Applications, 13(11), pp. 832-839, 2022.
  • [25] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D.,Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” International Journal of Computer Vision, 128 (2), pp. 336-359, 2019.
  • [26] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in Proc. IEEE International Conference on Computer Vision, 2017, pp. 2961.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme
Bölüm Makaleler
Yazarlar

Özlü Dolma 0000-0002-3947-898X

Erken Görünüm Tarihi 6 Aralık 2023
Yayımlanma Tarihi 19 Aralık 2023
Gönderilme Tarihi 27 Ekim 2023
Kabul Tarihi 5 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 2

Kaynak Göster

IEEE Ö. Dolma, “COVID-19 and Non-COVID-19 Classification from Lung CT-Scan Images Using Deep Convolutional Neural Networks”, IJMSIT, c. 7, sy. 2, ss. 53–60, 2023.