Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

COVID-19 Diagnosis Using Machine Learning


Affiliations
1 Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, India
2 Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, India
3 Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, India
 

Over 4 million individuals have already died as a result of the deadly contagious viral COVID-19 worldwide. The infection can seriously harm the lungs, increasing the chance of fatal health effects. The only way to lower the mortality rate due to this deadly illness and to halt its growth is through early detection. Deep learning has recently come to light as one of the most useful methods for computer aided diagnosis for helping clinicians make correct illness diagnoses. However, deep learning models require a lot of processing, so hardware with TPUs and GPUs is required to execute these models. To create machine learning models that can be used on mobile and peripheral devices, experts are currently working. In this context, the goal of this study is to create a concise Convolution Neural Network-based computer-aided diagnostic system that can be used on devices with limited processing capacity, such as mobile phones and iPads, to identify the presence of the Covid-19 virus in x-ray pictures. On the basis of various assessment parameters, the findings plainly show that the suggested model outperforms other transfer learning models such as Resnet50, Inception, and Xception. According to various evaluation parameters, the findings definitely show that the proposed model outperforms other transfer learning models like Resnet50, Inception, and Xception.

Keywords

Deep Learning, CNN, COVID-19, Transfer Learning, Image Enhancement.
User
Notifications
Font Size

  • S. Sharma, M.Sharma and G. Singh, “A chaotic and stressed environment for 2019-nCoV suspected, infected and other people in India: Fear of mass destruction and causality,” Asian J. Psychiatry, vol. 51, p. 102049, 2020. doi:10.1016/j.ajp.2020.102049.

  • C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, & Z. Xie, “Deep learning and its applications in biomedicine,” Genomics Proteomics Bioinformatics, vol. 16, no. 1, pp. 17-32, 2018.

  • J. N. Kanji , “False negative rate of COVID-19 PCR testing: A discordant testing analysis,” Virol. J., vol. 18, no. 1, pp. 1-6, 2021. doi:10.1186/s12985-021-01489-0.

  • Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” Lect. Notes Comput. Sci. (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7700 LECTU, pp. 437-478, 2012.

  • L. Liu, J. Xu, Y. Huan, Z. Zou, S. C. Yeh, & L. R. Zheng, “A smart Dental Health-IoT platform based on intelligent hardware, deep learning, and mobile terminal,” IEEE J. Biomed. Health Inform., vol. 24, no. 3, pp. 898-906, 2020. doi:10.1109/JBHI.2019.2919916.

  • A. Maier, C. Syben, T. Lasser, & C. Riess, “A gentle introduction to deep learning in medical image processing,” Z. Med. Phys., vol. 29, no. 2, pp. 86-101, 2019. doi:10.1016/j.zemedi.2018.12.003.

  • A. Krizhevsky, I. Sutskever, & G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84-90, 2017.

  • M. Toğaçar , B. Ergen, Z. Cömert, & F. Özyurt, “A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models,” IRBM, vol. 41, no. 4, pp. 212-222, 2020. doi:10.1016/j.irbm.2019.10.006.

  • T. Chitnis, B. I. Glanz, C. Gonzalez, B. C. Healy, T. J. Saraceno, N. Sattarnezhad, C. D. Cruz, M. P. Turcsanyi, “Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis,” npj Digit. Med., vol. 2, article number 123, 2019. doi:10.1038/s41746-019-0197-7.

  • G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, , M. Ghafoorian & C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal., vol. 42, pp. 60-88, 2017. doi:10.1016/j.media.2017.07.005.

  • D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S.L. Baxter, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, no. 5, p. 1122-1131.e9, 2018. doi:10.1016/j.cell.2018.02.010.

  • C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, & Z. Xie, “Deep learning and its applications in biomedicine,” Genomics Proteomics Bioinformatics, vol. 16, no. 1, pp. 17-32, 2018. doi:10.1016/j.gpb.2017.07.003.

  • A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, & J. J. P. C. Rodrigues, “Identifying pneumonia in chest X-rays: A deep learning approach,” Meas. J. Int. Meas. Confed ., vol. 145, pp. 511-518, 2019.

  • D. Singh, V. Kumar, Vaishali & M. Kaur, “Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks,” Eur. J. Clin. Microbiol. Infect. Dis., vol. 39, no. 7, pp. 1379-1389, 2020. doi:10.1007/s10096-020-03901-z.

  • N. Narayan Das, N. Kumar, M. Kaur, V. Kumar & D. Singh, “Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays,” IRBM, 2020. doi:10.1016/j.irbm.2020.07.001.

  • L. Brunese, F. Mercaldo, A. Reginelli, & A. Santone, “Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays,” Comput. Methods Programs Biomed., vol. 196, p. 105608, 2020. doi:10.1016/j.cmpb.2020.105608.

  • R. Patel and A. Chaware, “Transfer learning with fine-tuned MobileNetV2 for diabetic retinopathy.” International Conference for Emerging Technology, INCET 2020, vol. 2020, 2020, pp. 6-9.

  • M. N. Wahab , A. Nazir, A.T.Z. Ren, M.H.M. Noor, M.F. Akbar, and A.S.A. Mohamed, “Efficientnet-lite and hybrid CNN-KNN implementation for facial expression recognition on raspberry pi,” IEEE Access, vol. 9, pp. 134065-134080, 2021.

  • Available at: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.

  • D. Ezzat and H. A. Ella, “An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization,” Appl. Soft Comput., vol. 98, p. 106742, 2021. doi:10.1016/j.asoc.2020.106742.


Abstract Views: 118

PDF Views: 0




  • COVID-19 Diagnosis Using Machine Learning

Abstract Views: 118  |  PDF Views: 0

Authors

Harpreet Singh
Assistant Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology Patiala, India
Balraj Preet Kaur
Research Scholar, Department of Computer Science and Engineering, DAV University, Jalandhar, India
Rahul Hans
Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, India
Sanjeev Sharma
Associate Professor, Department of Computer Science and Engineering, DAV University, Jalandhar, India

Abstract


Over 4 million individuals have already died as a result of the deadly contagious viral COVID-19 worldwide. The infection can seriously harm the lungs, increasing the chance of fatal health effects. The only way to lower the mortality rate due to this deadly illness and to halt its growth is through early detection. Deep learning has recently come to light as one of the most useful methods for computer aided diagnosis for helping clinicians make correct illness diagnoses. However, deep learning models require a lot of processing, so hardware with TPUs and GPUs is required to execute these models. To create machine learning models that can be used on mobile and peripheral devices, experts are currently working. In this context, the goal of this study is to create a concise Convolution Neural Network-based computer-aided diagnostic system that can be used on devices with limited processing capacity, such as mobile phones and iPads, to identify the presence of the Covid-19 virus in x-ray pictures. On the basis of various assessment parameters, the findings plainly show that the suggested model outperforms other transfer learning models such as Resnet50, Inception, and Xception. According to various evaluation parameters, the findings definitely show that the proposed model outperforms other transfer learning models like Resnet50, Inception, and Xception.

Keywords


Deep Learning, CNN, COVID-19, Transfer Learning, Image Enhancement.

References