ML based prediction of COVID-19 diagnosis using statistical tests

Yıl 2023, Cilt: 65 Sayı: 2, 79 - 99, 29.12.2023

Şifa Özsarı Fatma Zehra Ortak Mehmet Serdar Güzel Mükerrem Bahar Başkır Gazi Erkan Bostancı

https://doi.org/10.33769/aupse.1227857

Öz

The first case of the novel Coronavirus disease (COVID-19), which is a respiratory disease, was seen in Wuhan city of China, in December 2019. From there, it spread to many countries and significantly affected human life. Deep learning, which is a very popular method today, is also widely used in the field of healthcare. In this study, it was aimed to determine the most suitable Deep Learning (DL) model for diagnosis of COVID-19. A popular public data set, which consists of 2482 scans was employed to select the best DL model. The success of the models was evaluated by using different performance evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, kappa and AUC. According to the experimental results, it has been observed that DenseNet models, AdaGrad and NADAM optimizers are effective and successful. Also, whether there are statistically significant differences in each performance measure/score of the architectures by the optimizers was observed with statistical tests.

Anahtar Kelimeler

COVID-19, deep learning, CT images, statistical analysis

Kaynakça

• Velavan, T. P., Meyer, C. G., The COVID-19 epidemic, TM & IH, 25 (3) (2020), 278, https://doi.org/10.1111/tmi.13383.
• Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., Soufi, G. J., Deep-covid: Predicting covid-19 from chest X-ray images using deep transfer learning, Med. Image Anal., 65 (2020), 101794, https://doi.org/10.1016/j.media.2020.101794.
• Amyar, A., Modzelewski, R., Li, H., Ruan, S., Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation, Comput. Biol. Med, 126 (2020), 104037, https://doi.org/10.1016/j.compbiomed.2020.104037.
• Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, O., Sun, Z., Xia, L. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases, Radiology, 296 (2020), E32-E40, https://doi.org/10.1148/radiol.2020200642.
• Islam, M. M., Karray, F., Alhajj, R., Zeng, J., A review on deep learning techniques for the diagnosis of novel coronavirus (covid-19), IEEE Access, 9 (2021), 30551-30572, https://doi.org/10.1109/ACCESS.2021.3058537.
• Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, O., Chen, Y., Su, J., et al., A deep learning system to screen novel coronavirus disease 2019 pneumonia, Engineering, 6 (10) (2020), 1122-1129, https://doi.org/10.1016/j.eng.2020.04.010.
• Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al., A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19), Eur. Radiol., (2021), 1-9, https://doi.org/10.1007/s00330-021-07715-1.
• Kanne, J. P., Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist, Radiological Society of North America, (2020), https://doi.org/10.1148/radiol.2020200241.
• Rubin, G. D., Ryerson, C. J., Haramati, L. B., Sverzellati, N., Kanne, J. P., Raoof, S., Schluger, N. W., Volpi, A., Yim, J. J., Martin, I. B. K., et al., The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society, Radiology, 296 (11) (2020), 172-180, https://doi.org/10.1016/j.chest.2020.04.003.
• Kanne, J. P., Little, B. P., Chung, J. H., Elicker, B. M., Ketai, L. H., Essentials for radiologists on COVID-19: an update—radiology scientific expert panel, Radiological Society of North America, (2020), https://doi.org/10.1148/radiol.2020200527.
• Bhattacharya, S., Maddikunta, P. K. R., Pham, Q. V., Gadekallu, T. R., Chowdhary, C. L., Alazab, M., Piran Md. J., et al., Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey, Sustain. Cities Soc., 65 (2021), 102589, https://doi.org/10.1016/j.scs.2020.102589.
• Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D., Shi, Y., Lung infection quantification of COVID-19 in CT images with deep learning, arXiv preprint arXiv:2003.04655, (2020), https://doi.org/10.1002/mp.14609.
• Huang, L., Han, R., Ai, T., Yu, P., Kang, H., Tao, Q., Xia, L., Serial quantitative chest CT assessment of COVID-19: a deep learning approach, Radiology: Cardiothoracic Imaging, 2 (2) (2020), e200075, https://doi.org/10.1148/ryct.2020200075.
• Wu, X., Hui, H., Niu, M., Li, L., Wang, L., He, B., Yang, X., Li, L., Li, H., Tian, J., and others, Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study, Eur. J. Radiol., 128 (2020), 109041, https://doi.org/10.1016/j.ejrad.2020.109041.
• Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., Mohammadi, A., Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks, Comput. Biol. Med., 121 (2020), 103795, https://doi.org/10.1016/j.compbiomed.2020.103795.
• Apostolopoulos, I. D., Mpesiana, T. A., Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Eng. Sci. Med., 43 (2) (2020), 635-640, https://doi.org/10.1007/s13246-020-00865-4.
• Singh, D., Kumar, V., Kaur, M., and others, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks, Eur. J. Clin. Microbiol. Infect. Dis., 39 (7) (2020), 1379-1389, https://doi.org/10.1007/s10096-020-03901-z.
• Khan, A. I., Shah, J. L., Bhat, M. M., CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images, Comput. Meth. Prog. Bio., 196 (2020), 105581, https://doi.org/10.1016/j.cmpb.2020.105581.
• Krizhevsky, A., Sutskever, I., Hinton, G. E., Imagenet classification with deep convolutional neural networks, Adv Neural Inf Process Syst, 25 (2012), 1097-1105, https://doi.org/10.1145/3065386.
• Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, (2014), https://doi.org/10.48550/arXiv.1409.1556.
• Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., Keutzer, K., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size, arXiv preprint arXiv:1602.07360, (2016), https://doi.org/10.48550/arXiv.1602.07360.
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., Going deeper with convolutions, CVPR, (2015), 1-9, https://doi.org/10.48550/arXiv.1409.4842.
• Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C., Mobilenetv2: Inverted residuals and linear bottlenecks, CVPR, (2018), 4510-4520, https://doi.org/10.48550/arXiv.1801.04381.
• He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition, CVPR, (2016), 770-778, https://doi.org/10.48550/arXiv.1512.03385.
• Chollet, F., Xception: Deep learning with depthwise separable convolutions, CVPR, (2017), 1251-1258, https://doi.org/10.48550/arXiv.1610.02357.
• Jaiswal, A., Gianchandani, N., Singh D., Kumar, V., Kaur, M., Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning, J. Biomol. Struct. Dyn., (2017), 4700-4708, https://doi.org/10.1080/07391102.2020.1788642.
• Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q., Densely connected convolutional networks, CVPR, (2020), 1-8, https://doi.org/10.48550/arXiv.1608.06993.
• Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C., A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT, IEEE Trans. Med. Imaging., 39 (8) (2020), 2615-2625, https://doi.org/10.1109/TMI.2020.2995965.
• Ronneberger, O., Fischer, P., Brox, T., U-net: Convolutional networks for biomedical image segmentation, MICCAI, (2015), 234-241, https://doi.org/10.48550/arXiv.1505.04597.
• Kingma, D. P., Ba, J., Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, (2014), https://doi.org/10.48550/arXiv.1412.6980.
• Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Huang, S., Yang, M., Yang, X., et al., Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography, Sci. Rep., 10 (1) (2020), 1-11, https://doi.org/10.1038/s41598-020-76282-0.
• Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., Liang, J., Unet++: A nested u-net architecture for medical image segmentation, DLMIA and ML-CDS, (2018), 3-11, https://doi.org/10.48550/arXiv.1807.10165.
• Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Wang, R., Zhao, H., Zha, Y., et al., Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images, TCBB, (2021), https://doi.org/10.1109/TCBB.2021.3065361.
• Gozes, O., Frid-Adar, M., Greenspan, H., Browning P. D., Zhang, H., Ji, W., Bernheim, A., Siegel, E., Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis, arXiv preprint arXiv:2003.05037, (2020), https://doi.org/10.48550/arXiv.2003.05037.
• He, X., Yang, X., Zhang, S., Zhao, J., Zhang, Y., Xing, E., Xie, P., Sample-efficient deep learning for COVID-19 diagnosis based on CT scans, medrxiv, (2020), https://doi.org/10.1101/2020.04.13.20063941.
• Soares, E., Angelov, P., Biaso, S., Froes, M. H., Abe, D. K., SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification, medRxiv, (2020) https://doi.org/10.1101/2020.04.24.20078584.