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Big Data Mining and Analytics  2021, Vol. 4 Issue (2): 84-93    DOI: 10.26599/BDMA.2020.9020012
Special Section on Artificial Intelligence and Big Data Analytics for Coronavirus (COVID-19)     
Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network
Krishna Kant Singh(),Akansha Singh*()
Department of ECE, KIET Group of Institutions, Delhi-NCR, Ghaziabad 201206, India
Department of CSE, ASET, Amity University Uttar Pradesh, Noida 201310, India
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Abstract  

Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The severity of the disease can be understood by the massive number of deaths and affected patients globally. If the diagnosis is fast-paced, the disease can be controlled in a better manner. Laboratory tests are available for diagnosis, but they are bounded by available testing kits and time. The use of radiological examinations that comprise Computed Tomography (CT) can be used for the diagnosis of the disease. Specifically, chest X-Ray images can be analysed to identify the presence of COVID-19 in a patient. In this paper, an automated method for the diagnosis of COVID-19 from the chest X-Ray images is proposed. The method presents an improved depthwise convolution neural network for analysing the chest X-Ray images. Wavelet decomposition is applied to integrate multiresolution analysis in the network. The frequency sub-bands obtained from the input images are fed in the network for identifying the disease. The network is designed to predict the class of the input image as normal, viral pneumonia, and COVID-19. The predicted output from the model is combined with Grad-CAM visualization for diagnosis. A comparative study with the existing methods is also performed. The metrics like accuracy, sensitivity, and F1-measure are calculated for performance evaluation. The performance of the proposed method is better than the existing methodologies and thus can be used for the effective diagnosis of the disease.



Key wordscoronavirus      COVID-19      deep learning      convolution neural network      X-Ray images     
Received: 05 June 2020      Published: 03 March 2021
Corresponding Authors: Akansha Singh     E-mail: krishnaiitr2011@gmail.com;akanshasing@gmail.com
About author: Krishna Kant Singh is an associate professor in electronics & communication engineering at KIET Group of Institutions, Delhi-NCR, India. He is a senior member of IEEE. He has wide teaching and research experience. He has acquired BTech, MTech, and PhD (IIT Roorkee) degrees in the area of deep learning and remote sensing. He has authored more than 70 technical books and research papers in international conferences and SCIE journals of repute. He is the associate editor of IEEE Access and Journal of Intelligent & Fuzzy System. He is also the editorial board member of Applied Computing and Geosciences.|Akansha Singh is an associate professor in computer science engineering at Amity University Uttar Pradesh, Noida, India. She has wide teaching and research experience. She has acquired BTech, MTech, and PhD (IIT Roorkee) degrees in the area of neural network and remote sensing. She has authored more than 70 technical books and research papers in international conferences and SCIE journals of repute. Her area of interest includes mobile computing, artificial intelligence, deep learning & amp, and digital image processing. She is the associate editor of IEEE ACCESS and the guest editor of Open Computer Science.
Cite this article:

Krishna Kant Singh,Akansha Singh. Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network. Big Data Mining and Analytics, 2021, 4(2): 84-93.

URL:

http://bigdata.tsinghuajournals.com/10.26599/BDMA.2020.9020012     OR     http://bigdata.tsinghuajournals.com/Y2021/V4/I2/84

Fig. 1 Overview of COVID-19.
Fig. 2 Proposed methodology.
Layer typeOutput shapeNumber of parametersKernel sizeDropoutNumber of filters
Input(224,224,3)0-0-
Wavelet Lambda(112,112,12)03×304
Separable Conv 2dx2 (ReLU)(14,14,256)34363×3032
Batch normalization(14,14,256)1024-0-
Maxpooling 2d(7,7,256)0-0-
Separable Conv 2dx2 (ReLU)(7,7,256)68 0963×3064
Batch normalization(7,7,256)68 096-0-
Maxpooling 2d(7,7,256)0-0.2-
Separable Conv 2dx2 (ReLU)(7,7,256)10243×30128
Batch normalization(7,7,256)512-0-
Maxpooling 2d(7,7,256)0-0.2-
Separable Conv 2dx2 (ReLU)(7,7,256)102 2723×30256
Batch normalization(7,7,256)1024-0-
Maxpooling 2d(3,3,256)0-0.2-
Separable Conv 2dx2 (ReLU)(3,3,256)133 8883×30256
Batch normalization(3,3,256)1024-0-
Maxpooling 2d(3,3,256)0-0.2-
Separable Conv 2dx2 (ReLU)(3,3,512)267 2643×30512
Batch normalization(3,3,512)2048-0-
Maxpooling 2d(1,1,512)0-0.2-
FC1 (ReLU)(512)262 6560.7512
FC2 (ReLU)(128)65 6640.5128
FC3 (ReLU)(64)82560.364
FC4 (ReLU)(32)20800.232
FC5 (ReLU)(3)9903
Table 1 Model summary.
Image typeTrainTest
Normal542136
Viral pneumonia503126
COVID-1910626
Total1151288
Table 2 Distribution of images in train and test sets.
9].
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Fig. 3 Sample images of normal, viral pneumonia, and COVID-19 infected patients[9].
Disease typePredicted result
NormalCOVID-19Viral pneumonia
Normal13015
COVID-191241
Viral pneumonia31122
Table 3 Confusion matrix.
Disease typeAccuracyPrecisionSensitivityF1-score
Normal96.53979696
COVID-1998.61929292
Viral pneumonia96.53959796
Table 4 Value for the proposed method. (%)
Fig. 4 Grad-CAM visualization of (a) normal, (b) COVID-19, and (c) viral pneumonia.
MethodAccuracyPrecisionSensitivityF1-score
DarkCovidNet87.0289.9685.3587.37
Flat-EfficientNet B393.3493.9393.9693.94
Hierarchical-EfficientNet B393.5193.9393.5593.73
DeTraC-ResNet1895.1293.3697.9195.58
Proposed95.8395.6796.0795.63
Table 5 Comparative analysis. (%)
Fig. 5 Comparative study.
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