# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 2 Related Work  
The current section presents a considerable amount of work, found in the literature, that explores the role of artificial intelligence in the automated diagnosis of COVID-19 using chest X-ray images, and distinguishing it from pneumonia and healthy cases. A deep learning framework was developed, termed COVID-CheXNet that could detect COVID-19 using chest X-rays [26]. First, image pre-processing, using contrast-limited adaptive histogram equalization and a Butterworth Band-pass filter. Following this, two parallel deep learning networks were applied, one of which was ResNet34 and the other comprised the high-resolution network, HRNet. The image enhancement module consisted of applying the adaptive contrast enhancement method using CLAHE. The CLAHE method enhances the contrast and edges of the image. Moreover, a Butterworth filter was applied for noise reduction. The train-test distribution of the dataset was 70% to 30%. The ResNet model [27] was pre-trained on ImageNet, while the HRNet model was trained on the subject dataset. The two models, HRNet and Resnet34, combined score-level fusion and decision-level fusion. Using WSR in the score-level fusion, the best results were obtained with an accuracy of 99.99%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012%, where WSR represents the Weighted Sum Rule in which weights are assigned to the individual scores and can be adjusted related to the performance of the individual level performance to obtain overall optimal performance. A computer-aided diagnosis (CAD) scheme was developed that involved several image pre-processing algorithms: removing diaphragms from the X-ray images, normalizing the contrast-to-noise ratio of the image, generating three different images from the input dataset, and feeding them to VGG16 CNN. The test set and train set had a ratio of 90:10. The images were converted to binary form, followed by morphological operations and the higher intensity component; the diaphragm region was removed from the images. Before feeding the images to the CNN, three different images were generated after diaphragm removal. The first image was generated by applying bilateral filtering to remove the noise. To generate the second image, the histogram equation method was applied, normalizing the image, and thereby enhancing lung tissue patterns and characteristics concerning COVID-19. The last image was fed to the network as it was. The three images were resized to 224 \(\times\) 224 pixels and given as inputs to the VGG16 model. The top layer of VGG16 was removed and two FC layers were added with 256 and 128 nodes using ReLU. Finally, a classification layer, Softmax, was added. The accuracy obtained was 94% in classifying three classes and the accuracy of diagnosing COVID-19 was 98.6%. The model was compiled with a batch size of 4, maximum epochs of 200, and an initial learning rate of \(10^{-5}\); with loss value [28]. An automated deep learning method was developed for an early diagnosis of COVID-19 from chest X-ray images using transfer learning. The models tested for evaluation were Inception-ResNetV2, InceptionNetV3 and NASNetLarge. The training and test datasetratio was 80:20. InceptionNetV3 gave the best performance with accuracies of 98.63% and 99.02% with and without using data augmentation on the training data. Inception-ResNetV2 and InceptionNetV3 were equal and produced the highest accuracy of 97.87% with data augmentation, while NASNetLarge exceeded in performance without a data augmentation strategy, obtaining an accuracy of 94.87% [29]. An approach for COVID-19 detection using radiography images and comprising three phases was proposed. The first phase detected the presence of pneumonia, while the second phase differentiated between COVID-19 and pneumonia. The third phase localized the areas generating a map where COVID-19 was present.