## 4 Results
### CovidDetNet Experimental Setup
#### 4.3.4 COVID-19 Detection Comparison with State-of-the-Art Methods  
This experiment aims to recover the performance of the proposed CovidDetNet framework in identifying and classifying COVID-19 from chest radiograph images compared to existing state-of-the-art deep learning approaches in the literature. We compared the proposed classification model performance to various approaches [43; 44; 68; 69]. [PERSON] et al. [68] proposed an automated diagnostic method for COVID-19 detection and classification using the DL model on chest radiograph images. The Inception-V3 model, with node dropping, flattening, dense layer, and normalization, was used to automatically present a transfer learning-based algorithm for detecting COVID-19 from chest radiographs. Three separate COVID-19 X-rays datasets with three classes (COVID-19, normal, and pneumonia) were used to test the model's effectiveness. The suggested framework has achieved a maximum accuracy of 97.7%. Similarly, [PERSON] et al. presented the SARS-Net DL model, an automatic method for COVID-19 detection using graph convolutional networks and CNNs for identifying anomalies in a patient's chest radiographs for the presence of COVID-19 virus [69]. The suggested model also outperformed the state-of-the-art methodologies previously discussed. On the validation set, the suggested model had an accuracy of 97.60% and a sensitivity of 92.90%. [PERSON] et al. [43] evaluated eleven deep CNN frameworks for the classification of chest X-rays into healthy people, people with COVID-19, and pneumonia. They focus on three distinct adjustments to adapt the designs for the COVID-19 classification task by adding new layers to them. The proposed techniques were tested and analyzed on a chest X-ray images dataset, with the best-performing setup achieving the maximum classification accuracy of 98.04% and the highest F1-score of 98.22%. [PERSON] et al. [44] introduced a CNN framework for detecting COVID-19 using chest radiographs that are faster and more reliable. For feature extraction, a CNN approach was utilized. Four convolutional layers, three MaxPooling layers, one flattened layer, and two thick layers with a ReLu activation function make up the model. In the final layers, pre-trained models such as InceptionV3, Resnet50, MobileNetV2, and VGG16 were employed with some modifications. The created model had a validation accuracy of 98%. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Measure** \\ \hline Alexnet & 96.13 & 93 & 92.33 & 97.07 & 92.66 \\ Resnet18 & 97.14 & 94.66 & 94.66 & 97.43 & 94.66 \\ Squeezenet & 95.99 & 93 & 93.33 & 96.87 & 93.16 \\ Darknet19 & 96.87 & 93.33 & 94.33 & 97.43 & 93.83 \\ Googlenet & 95.82 & 92 & 93 & 96.83 & 93 \\ shufflenet & 96.09 & 92.33 & 93.33 & 97.61 & 92.83 \\ Mobilenetv2 & 96.29 & 93.33 & 93.33 & 96.81 & 93.33 \\ Inceptionv3 & 95.05 & 90.33 & 92.0 & 95.76 & 91.16 \\ Proposed & 98.40 & 97 & 96.66 & 97.06 & 96.82 \\ CovidDetNet & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: COVID-19 detection using chest radiographs comparison with hybrid models. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**S. No** & **CNN Architecture** & **Input Size** & **Depth** & **Activation Layer Name** & **Features** \\ \hline
1 & Alexnet & 227 \(\times\) 227 & 8 & fc8 & 1000 \\
2 & Resnet18 & 224 \(\times\) 224 & 18 & pool5 & 512 \\
3 & Squeezenet & 227 \(\times\) 227 & 18 & pool10 & 1000 \\
4 & Darknet19 & 256 \(\times\) 256 & 19 & avg1 & 1000 \\
5 & Googlenet & 224 \(\times\) 224 & 22 & pool5-drop\_7 \(\times\) 7\_s1 & 1024 \\
6 & Shufflenet & 224 \(\times\) 224 & 50 & node\_200 & 544 \\
7 & Resnet50 & 224 \(\times\) 224 & 50 & avg\_pool & 2048 \\
8 & Mobilnetv2 & 224 \(\times\) 224 & 53 & global\_average\_pooling2d\_1 & 1280 \\
9 & Inceptionv3 & \(299\times 299\) & 48 & avg\_pool & 2048 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Deep features extractor models details.