Development Of A Deep Learning Model To Classify X-Ray Of Covid-19, Normal And Pneumonia-Affected Patients

A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images

“…In addition, Law and Lin [44] implemented Transfer learning on a dataset with 1200 images of COVID-19 patients and found that VGG-16 gives superior performance metrics as compared to the other ResNet models. A Multi-model fusion study was performed by Cengil and Cinar [45] , tested various models namely AlexNet, EfficientNet-b0, NASNetLarge and Xception to find the best performing amalgamations. Khan et al [23] used pre-trained Xception architecture to get 95% 3-class and 89.6% 4-class accuracy on a dataset of 284 COVID-19, 310 healthy, 227 Viral Pneumonia and 330 Bacterial Pneumonia samples.…”

“…All the performance metrics can be seen in the Table 3 and show class wise results and improvement after the fusion of the parent models clearly. Most of the existing works focus on 3-class and binary classification [45] , [44] , [15] , [17] , [18] tasks. Our study, extends the 3-class classification task to 4-class classification and continues to show significant improvements.…”

“…Most of the existing works focus on 3-class and binary classification [45] , [44] , [15] , [17] , [18] tasks. Our study, extends the 3-class classification task to 4-class classification and continues to show significant improvements.…”