# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 2 Related Work
The authors employed the VGG16 model using pre-trained weights on ImageNet; they froze the FC layer head and added new layers, namely, AveragePooling2D, flatten, dense, dropout, and a dense layer using the Softmax function. Data augmentation was applied by rotating the image by 15 degrees clockwise or counterclockwise. The authors used the gradient-weighted class activation mapping (Grad-CAM) algorithm to visualize the areas infected with COVID-19. Grad-CAM works by looking into the final convolutional layer and then examining the gradient information flowing into that layer, thereby generating a heatmap. A cross-validation technique was employed by the authors. The experiments were performed on a dataset of 6523 X-ray images, achieving an average accuracy of 97% [30]. A study [31] involving feature extraction using deep learning and transfer learning, while feeding the features into an SVM classifier to classify the COVID-19 and normal cases was performed. The dataset contained 180 COVID-19 and 200 normal cases. The deep learning models used were ResNet18, ResNet50, ResNet101, VGG16, and VGG19 [32]; different SVM kernel functions namely, linear, quadratic, cubic, and Gaussian were employed. The ResNet50 with the linear kernel function on SVM produced the best results, with an accuracy of 94.7%. Also, a customized CNN model was applied with end-to-end training on the dataset, producing an accuracy of 91.6%, and a fine-tuned ResNet50 model achieved an average accuracy of 92.6%. Following this, the authors deployed SVM on local texture descriptors, which achieved lower accuracy than the feature extraction using deep learning and then applying SVM. The proposed convolution model with 21 layers was trained with a learning rate of 0.001 and over 300 iterations, producing an accuracy of 91.58%. A study was undertaken where an ensemble of three deep learning models was used. The CNN models used were DenseNet201, ResNet50V2, and InceptionV3. An ensemble of the prediction of these three models to generate the final classification. The proposed approach achieved higher accuracy than the other three individual techniques, giving an accuracy of 95.7%, and a sensitivity of 98% on the test set. Two architectures (one for binary classification and the other for multi-class (3 classes) classification) comprising an ensemble classification embedded with transfer learning and CNN layers were proposed. The variation between these two architectures was the usage of the Sigmoid function instead of Softmax in the classification layer. The accuracy achieved for multi-classification was 99.21%; for binary-classification, it was 96.15% [33]. A modified VGG-19 architecture with transfer learning was deployed for the prediction of COVID-19 based on chest X-rays, patient ID, sex, age, diagnosis (COVID-19 or not), survival (yes or no), scan view (e.g., posteroanterior, supine anteroposterior), timestamp, and location The dataset comprised 181 COVID-19 and 364 normal chest X-ray images. Transfer learning was deployed for the pre-modified part and achieved an accuracy of 96.3% [34]. A medical decision support system was developed using convolutional neural network with EfficientNetB4 as the base architecture for the diagnosis of COVID-19. The entire model was trained on chest X-ray images. The efficiency achieved for binary classification was 99.62%, and for multi-class classification it was 97.11%. A pre-trained Xception model with transfer learning was used with a custom deep CNN model. The obtained accuracy in the test phase was 97.4%. The dataset comprised 127 COVID-19 cases, 500 pneumonia, and 500 normal chest X-ray images [35]. In another study [36], a simple CNN, deep learning model was devised and a modified pre-trained AlexNet network was deployed for distinguishing between normal and COVID-19 cases. The CNN architecture compromised one convolutional layer with only 16 filters, one batch normalization layer, a RELU layer, two fully connected layers, and finally, a SoftMax layer for classification.