Abstract
In this paper, an efficient classification model for classifying COVID-19 based on X-ray and computed tomography (CT) images were introduced. Medical dataset images were acquired from available open sources. Chest X-ray and CT images are considered critical diagnostic tools, especially in the scarcity of reverse transcription-polymerase chain reaction (RT-PCR) test kits. Routinely, for the detection of pneumonia, doctors frequently use X-rays of the chest to analyze the infection quickly. Deep learning is used successfully as a tool for machine learning, where a neural network is capable of automatically learning features. Among deep learning techniques, deep convolutional networks are actively used for medical image analysis. The proposed approach modified the well-known convolutional neural network, named AlexNet, to reduce the total computations and keep the model's accuracy. The proposed LightWeight Deep Convolutional Neural Network (LWCOV) is finished with three layers of the fully connected 512 SoftMax. Tests have proven satisfactory classification results compared to some recent models, and it is superior in calculation speed, which saves resource consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Poostchi, M., Silamut, K., Maude, R., Jaeger, S., Thoma, G.: Image analysis and machine learning for detecting malaria. Trans. Res. 194, 36–55 (2018)
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K.: Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
Nijhawan, R., Verma, R., Bhushan, S., Dua, R., Mittal, A.: An integrated deep learning framework approach for nail disease identification. In: Proceedings of 13th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp. 197–202. IEEE (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261 (2016)
Hua, K.L., Hsu, C.H., Hidayati, S.C., Cheng, W.H., Chen, Y.J.: Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and Therapy, vol. 8 (2015)
Gamal, M., Rizk, R., Mahdi, H., Elnaghi, B.E.: Osmotic bio-inspired load balancing algorithm in cloud computing. IEEE Access 7(1), 42735–42744 (2019)
Mohammed, N.H., Nashaat, H., Abdel-Mageid, S.M., Rizk, R.Y.: A framework for analyzing 4G/LTE-A real data using machine learning algorithms. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics (AISI2020), Cairo, Egypt, pp. 826–838 (2020)
Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K.: Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850 (2017)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)
Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.J., Fei-Fei, L.: Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8290–8299 (2018)
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shi, Y.: Lung infection quantification of covid-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655 (2020)
Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988 (2020)
Sethy, P.K., Behera, S.K.: Detection of coronavirus disease (covid-19) based on deep features. preprints 2020030300, p. 2020 (2020)
Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Khan, M.K.: Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. arXiv preprint arXiv:2004.00038 (2020)
Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2661–2671 (2019)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Nair, V., Hinton, G.E.: 3D object recognition with deep belief nets. In: Advances in Neural Information Processing Systems, pp. 1339–1347 (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Abd El-Rahiem, B., Ahmed, M.A.O., Reyad, O., Abd El-Rahaman, H., Amin, M., Abd El-Samie, F.: An efficient deep convolutional neural network for visual image classification. In: Proceedings of International Conference on Advanced Machine Learning Technologies and Applications, pp. 23–31. Springer, Cham (2019)
Shermin, T., Teng, S.W., Murshed, M., Lu, G., Sohel, F., Paul, M.: Enhanced transfer learning with imagenet trained classification layer. In: Pacific-Rim Symposium on Image and Video Technology, pp. 142–155. Springer, Cham (2019)
COVID-19 Open Research Dataset (CORD-19). https://pages.semanticscholar.org/coronavirus-research. Accessed 02 September 2020
SIRM COVID-19 Database. https://www.sirm.org/category/senza-categoria/covid-19/
COVID-19 BSTI Imaging Database. https://bsticovid19.cimar.co.uk/. Accessed 05 September 2020
COVID-19 Chest X-Ray Database. https://www.kaggle.com/tawsifurrahman/covid19-radiography-database. Accessed 10 September 2020
COVID-19 Chest X-Ray. https://www.kaggle.com/bachrr/covid-chest-xray. Accessed 12 September 2020
Eurorad. https://www.eurorad.org/. Accessed 07 September 2020
Dalia, E., Aboul, E.H., Hassan, A.E.: An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization. Applied Softcomputing, p. 106742 (2020). https://doi.org/10.1016/j.asoc.2020.106742
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
El-Baz, A., Saber, W., Rizk, R.Y. (2021). LWCOV: LightWeight Deep Convolutional Neural Network for COVID-19 Detection. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-69717-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69716-7
Online ISBN: 978-3-030-69717-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)