Automated detection of COVID-19 from X-ray images using CNN and Android mobile

被引：10
作者：
Bushra K.F. [1 ]
Ahamed M.A. [2 ,3 ]
Ahmad M. [4 ]
机构：
[1] Department of Biomedical Engineering, Chittagong University of Engineering & Technology, Chittagong
[2] Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna
[3] Bangladesh Telecommunications Company Limited, Dhaka
[4] Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna
来源：
Research on Biomedical Engineering | 2021年 / 37卷 / 03期
关键词：
Android mobile; CNN; COVID-19; X-ray image;
D O I：
10.1007/s42600-021-00163-2
中图分类号：
学科分类号：
摘要：
Purpose: The prevalence of the coronavirus disease 2019 (COVID-19) pandemic has made a huge impact on global health and the world economy. Easy detection of COVID-19 through any technological tool like a mobile phone can help a lot. In this research, we focus on detecting COVID-19 from X-ray images on Android mobile with the help of Artificial Intelligence (AI). Methods: A convolutional neural network (CNN) model is developed in MATLAB and then converted to the CNN model to TensorFlow Lite (TFLite) model to deploy on Android mobile. An Android application is developed which uses the TFLite model to detect COVID-19 using X-ray images. Results: By employing a 5-fold cross-validation, an average accuracy of 98.65%, sensitivity of 98.49%, specificity of 98.82%, precision of 98.81%, and F1-score of 98.65% are achieved in COVID-19 detection. Conclusion: With our developed Android application, users can detect COVID-19 from X-ray images on Android mobile, and it will be helpful for the diagnosis of COVID-19. © 2021, Sociedade Brasileira de Engenharia Biomedica.
引用
收藏
页码：545 / 552
页数：7
相关论文
共 32 条
[1]  
Abraham B., Nair M.S., Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier, Biocybern Biomed Eng, 40, pp. 1436-1445, (2020)
[2]  
Andersen K.G., Rambaut A., Lipkin W.I., Holmes E.C., Garry R.F., The proximal origin of SARS-CoV-2, Nat Med, 26, pp. 450-452, (2020)
[3]  
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, pp. 635-640, (2020)
[4]  
Chandra T.B., Verma K., Singh B.K., Jain D., Netam S.S., Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble, Expert Syst Appl, 165, (2021)
[5]  
This is a thread of COVID-19 CXR (All SARS-CoV-2 PCR+) from my hospital (Spain), (2020)
[6]  
Civit-Masot J., Luna-Perejon F., Dominguez Morales M., Civit A., Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images, Appl Sci, 10, (2020)
[7]  
Cohen J.P., Morrison P., Dao L., Roth K., Duong T.Q., Ghassemi M., COVID-19 Image Data Collection: Prospective Predictions are the Future., (2020)
[8]  
Cui J., Li F., Shi Z.-L., Origin and evolution of pathogenic coronaviruses, Nat Rev Microbiol, 17, pp. 181-192, (2019)
[9]  
El Asnaoui K., Chawki Y., Using X-ray images and deep learning for automated detection of coronavirus disease, J Biomol Struct Dyn, (2020)
[10]  
Elaziz M.A., Hosny K.M., Salah A., Darwish M.M., Lu S., Sahlol A.T., New machine learning method for image-based diagnosis of COVID-19, PLoS ONE, 15, (2020)
1234