## 4 Results
### CovidDetNet Experimental Setup
#### 4.3.3 Comparison with a Hybrid Approach  
A hybrid experiment for COVID-19 detection is elaborated in this section to examine the effectiveness of the proposed CovidDetNet classifier. It is claimed that putting an SVM classifier at the top of the network instead of traditional deep CNN increases classification performance significantly [32]. As a result, we devised a hybrid strategy in which we extracted in-depth features using the eight most well-known deep CNNs and then used these features as inputs to train SVM with the linear kernel (decision function). The values of Gamma and C hyperparameters are set to 0.1 and 1.0, respectively, because these settings yielded the maximum performance. In the proposed research, we employed Alexnet [56], Resnet18 [62], SqueezeNet [63], Darknet19 [64], Googlenet [65], Shufflenet [66], Resnet50 [62], Mobilnetv2 [67], and Inceptionv3 DL-based classification approaches.  
For this experiment, the dataset is divided into training and testing sets, i.e., we used 70% of the data for model training and 30% for testing. More specifically, we used all the 15,153 radiograph images (3616 data instances of COVID-19 patients, 1345 pneumonia radiographs, and 10,192 instances of healthy individuals) of the dataset named COVID-19 Radiography Database, where 10,606 images (2531 images of COVID-19 individuals, 941 Pneumonia radiographs, and 7134 images of healthy individuals) were used for training and remaining 4547 images (1085 images of COVID-19 individuals, 404 pneumonia radiographs, and 3058 images of healthy individuals) for testing. We used the same experimental setup (hyperparameters values are selected using the same approach as the proposed method) to train these models as mentioned in Table 2. Table 9 contains information about these deep feature extractor models. As different models need input images of different sizes, such as mobilenetv2 accepting 224-by-224 input images, whereas darknet19 requires 256-by-256 input images. The dataset images are automatically resized using augmented image data repositories before being inserted into the network for feature extraction. We employed activations on deeper layers (last fully connected or global average pooling layer) because they include more high-level information than earlier layers; for example, we applied activations on the fc8 layer, the last layer (FC layer) in Alexnet. These layers pool the input features overall spatial locations after applying the activation functions to provide distinct features (i.e., Shufflenet gives 1000 features in total). Table 10 shows the classification results of deep features and the SVM technique. Compared to CovidDetNet, deep features of all twelve networks and the SVM technique produced poorer accuracy outcomes. Based on the experimental results, the proposed CovidDetNet approach outperforms the other eight hybrid models, reaching a COVID-19 detection accuracy of 98.40%. The Resnet18 model had the second-highest accuracy of 97.14%, while Inceptionv3 had the lowest accuracy of 95.05%. Resnet18 outperforms other models with a recall of 94.66% in terms of true positive rate. It is to be notice that the accuracy of all hybrid comparative models is greater than 95%. Resnet18 attained the second-highest accuracy because it uses batch normalization to reduce generalization error. The proposed CovidDetNet approach successfully extracts more distinguishing features from the chest radiograph images, which is why the proposed approach achieved better results in detecting and identifying COVID-19. We used small filters with 3 \(\times\) 3 and 1 \(\times\) 1, which ensured the extraction of more detailed and robust features. The proposed model's batch normalization technique standardizes the inputs to a layer for each mini-batch, offers regularization, and decreases the generalization error.