Deep Learning-Based Approach for Diagnosis of COVID-19 Patients from CT Lung Images
Main Article Content
Abstract
COVID-19 is currently one of the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection plays an important role in identifying, making better decisions as well as ensuring treatment for the patients. In this study, we propose an approach to identify COVID-19 patients via deep learning methods. The proposed approach includes two steps: segmentation of lung and classification of patients via analyzing the segmented images. Especially, in the first step, the attention gate is integrated into Unet neural network structure, which can help increase the accuracy of the segmentation tasks. In the second step, the EfficientNet-B4 is applied to classify COVID-19 patients. By using EfficientNet, the performance of the classification process as well as the size of the model are improved. We apply the proposed approach for the database including 130 patients. Experimental results show the desired performances of the proposed approach.
Keywords
COVID-19, deep learning, segmentation, computed tomography, artificial intelligence
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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[4]. Feng, S., Jun, W., Jun, S., Ziyan W., Qian, W., Zhenyu, T., Kelei, H., Yinghuan, S., Dinggang, S.: Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. in IEEE Reviews in Biomedical Engineering, vol. 14, pp. 4-15, 2021, https://doi.org/10.1109/RBME.2020.2987975
[5]. Zhao, J., Zhang, Y., He, X., Xie, P., COVID-CT Dataset: A CT Scan Dataset about COVID-19. https://arxiv.org/abs/2003.13865 (2020).
[6]. Van-Truong Pham, Thi-Thao Tran, Pa-Chun Wang, Po-Yu Chen, Men-Tzung Lo: EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images. Artificial Intelligence In Medicine 115, 1-12 (2021). https://doi.org/10.1016/j.artmed.2021.102065
[7]. Wang, S., Shi, J., Ye, Z., Dong, D., Yu, D., Zhou, M., Liu, Y., Gevaert, O., Wang, K., Zhu, Y.: Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. European Respiratory Journal 53(3), 1800986 (2020). https://doi.org/10.1183/13993003.00986-2018
[8]. Walsh, S., Calandriello, L., Silva, M., Sverzellati, N.: Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. The Lancet Respiratory Medicine 6(11), 837–845 (2018). https://doi.org/10.1016/S2213-2600(18)30286-8
[9]. Choe, J., Lee, S., Do, K., Lee, G., Lee, J., Lee, S., Seo, J., Deep learning-based image conversion of ct reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 292(2), 365-373 (2019). https://doi.org/10.1148/radiol.2019181960
[10]. Wang, P., Xiao, X., Glissen Brown, J., Berzin, T., Tu, M., Xiong, F., ... Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2(10), 741-748 (2018). https://doi.org/10.1038/s41551-018-0301-3
[11]. Tan, M., Le, Q.: EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proc. 36th Int. Conf. Mach. Learn 2019, pp. 6105–6114
[12]. COVID-19 CT segmentation dataset. Available at: http://medicalsegmentation.com/covid19/.
[13]. Hofmanninger, J., Prayer, F., Pan, J., Röhrich, S., Prosch, H., Langs, G.: Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem. https://arxiv.org/abs/2001.11767 (2020). https://doi.org/10.1186/s41747-020-00173-2
[14]. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. 2015, pp. 234-241 https://doi.org/10.1007/978-3-319-24574-4_28
[15]. Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D.: Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis 53, 197–207 (2019). https://doi.org/10.1016/j.media.2019.01.012
[16]. Sadegh, S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Proc. Int. Workshop Mach. Learn. Med. Imag. 2017, pp. 379-387. https://doi.org/10.1007/978-3-319-67389-9_44