A Study on Detection of COVID-19 in Lung CT Images Using Transfer Learning With Resnet Pre-Trained Model
Keywords:
COVID-19 CT image classification, transfer learning, ResNet pre-trained modelAbstract
COVID-19 has impacted the lives of each and every person in the world. Diagnosis of COVID-19 using imaging systems can be integrated with the standard Reverse Transcription Polymerase Chain Reaction (RT PCR) test to perform the diagnosis more accurately. In this paper, transfer learning based method using a pre-trained deep neural network model viz., ResNet is proposed to classify COVID-19 computed tomography (CT) lung images. The pre-trained model is fine-tuned in order to make it learn the features specific to COVID-19 CT lung images. The proposed method is compared with the methods available in the literature. The results show that the proposed method is comparable to the existing methods.
Downloads
References
https://covid19.who.int/ (Accessed 8 February 2021) [2] https://covid19.who.int/region/searo/country/in (Accessed 8 February 2021)
T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun and L. Xia, “Correlation of chest CT and RT-PCR testing for coronavirus disease (COVID-19) in China: A report of 1014 cases”, Radiology, Vol. 296, No. 2, 2020, pp. 532 – 540.
A. Kovacs, P. Palasti, D. Verb, B. Bozsik, A. Palko and Z. T. Kincses, “The sensitivity and specificity of chest CT in the diagnosis of COVID-19”, European Radiology, 2020, DOI: 10.1007/500330-020-07347-X.
A. G. Floriano, A. F. Santiago, O. C. Nieto. and C. Y. Marquez, “A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images”, Computers and Electrical Engineering, Vol. 75, 2019, pp. 218 – 229.
N. Thakur and M. Juneja, “Classification of glaucoma using hybrid features with machine learning approaches”, Biomedical Signal Processing and Control, Vol. 62, 102137, 2020.
L. Gao and S. Wu, “Response score of deep learning for out of-distribution sample detection of medical images”, Journal of Biomedical Informatics, Vol. 107, 103442, 2020.
L. Mou, Y. Zhao, H. Fu, Y. Liu, J. Chang, Y. Zheng, P. Su, J. Yang and L. Chen, “CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging”, Medical Image Analysis, Vol. 67, 101874, 2021.
K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition”, in Proceedings of 2016 IEEE
Conference on Computer Vision and Pattern Recognition, 2016, pp. 770 – 778.
S. Bhattacharya, P. K. R. Maddikunta, Q. V. Pham, T. R. Gadekallu, S. Sivaramakrishnan, C. L. Chowdhary, M. Alazab and M. J. Piran, “Deep learning and medical image processing for corona virus (COVID-19) pandemic: A survey”, Sustainable Cities and Society, Vol. 5, 102589, 2021.
R. Jain, M. Gupta, S. Taneja and D. J. Hemanth, “Deep learning based detection and analysis of COVID-19 on chest X-ray images”, Applied Intelligence, Vol. 51, 2021, pp. 1690 – 1700.
M. A. Elaziz, K. M. Hosny, A. Salah, M. M. Darwish, S. Lu and A. Sahlol, “New machine learning method for image based diagnosis of COVID-19”, PLoS ONE, Vol. 15, No. 6, e0235187, 2020.
W. Zhang, B. Pogorelsky, M. Loveland and T. Wolf, “Classification of COVID-19 X-Ray images using a combination of deep and handcrafted features”, arXiv, 2101.07866, 2021.
V. Perumal, V. Narayanan and S. J. S. Rajasekar, “Detection of COVID-19 using CXR and CT images using transfer learning and Haralick features”, Applied Intelligence, Vol. 51, 2021, pp. 51341 – 51358.
A. Abbas, M. M. Abdelsamea and M. M. Gaber, “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network”, Applied Intelligence, Vol. 51, 2021, pp. 854 – 864.
A. Gupta, Anjum, S. Gupta and R. Katarya, “InstaCOvNet 19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray”, Applied Soft Computing Journal, Vol. 99, 106859, 2021.
A. J. Sahlol, D. Yousri, A. A. Ewees, M. A. A. Al-quanees, R. Domasevicus and M. A. Elaziz, “COVID-19 image classification using deep features and fractional-order marine predators algorithm”, Scientific Reports, Vol. 10, 15364, 2020.
J. D. Arias-Londono, J. A. Gomez-Garcia, L. Moro Velazquez and J. I. Godino-Llorente, “Artificial intelligence applied to chest X-ray images for the automatic detection of COVID-19: A thoughtful evaluation approach”, IEEE Access, Vol. 8, 2020, pp. 226811 – 226827.
Q. Yang, Y. Zhang, W. Dai and S. J. Pan, “Transfer learning”, Cambridge University Press, 2020.
A. Fandang, “Mastering TensorFlow 1.x”, Packt Publishing, 2018.
E. Soares, E. 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. DOI:10.1101/2020.04.24.20078584.
S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. K. Singh, Z. Xue, A. Karargyris, S. Antani, G. Thoma, C. J. McDonald, “Lung segmentation in chest radiographs suing anatomical atlases with nonrigid registration”, IEEE Transactions on Medical Imaging, Vo. 33, No. 2, 2014, pp. 577 – 590.
J. P. Cohen, P. Morrison and L. Dao, “COVID-19 image data collection”, arXiv: 2003.11597, 2020.
M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. Al-Emadi, M. B. I. Reaz and T. I. Islam, “Can AI help in screening viral and COVID-19 pneumonia?” IEEE Access, arXiv, 2003.13145, 2020.
G. Maguolo and L. Nanni, “A critic evaluation of methods for COVID-19 automatic detection from X-ray images, arXiv, 2004.12823, 2020.
