## 5 Discussion and Future Research Direction  
In this research, we have adopted a model with higher accuracy (98.40) than competing models in detecting COVID-19 from chest radiography. The training and testing accuracy of the model proliferates after each epoch, as shown in Figure 4, and the training and testing loss decrease gradually, as depicted in Figure 5. Although the prop approach yielded promising results, we recognized several limitations and made some recommendations for future research. Due to the unavailability of the research datasets that could be used as a baseline to investigate the severity level of COVID-19 infection, the proposed approach was unable to categorize the various stages of COVID-19 infection, such as pre-symptomatic, asymptomatic, moderate, and severe. The proposed method does not reveal how well the system detects COVID-19 using other imaging modalities such as computerized tomography (CT scans) and electrocardiogram (ECG) trace images. We continuously split image data into a 70% training set and a 30% test set in the proposed approach. Alternative splits, on the other hand, may produce different results. In the future, we plan to conduct experiments using comparable large-scale datasets of chest radiographs, CT scans, and ECG trace images to assess the models' generalization ability by testing them on a range of large-scale datasets from diverse sources and images obtained by several machines. We want to use the same method to categorize the various stages of COVID-19 infection, such as pre-symptomatic, asymptomatic, mild, severe, and so on. According to the experimental results, the proposed CovidDetNet method is more accurate than PCR because PCR results rely heavily on sample collection timing, type, storage, handling, and processing. A false-negative result is possible if the sample is not properly obtained or if an individual is tested too early after exposure to the virus or too late in their infection. Therefore, in the future, we intend to provide further experimental evidence to compare the performance of the proposed system (CovidDetNet approach) with PCR and other manual COVID-19 testing methods to identify its performance in identifying COVID-19. The proposed CovidDetNet approach estimated accuracy might be biased or inflated because we used a dataset containing images with only three classes (normal, pneumonia, covid) for training and testing the proposed model. Therefore, in the future, we intend to use the proposed method in other COVID-19 datasets or other medical datasets with CT scans or chest radiographs to test the generalization ability of the proposed CovidDetNet model so that it can be used in practice to detect different diseases such as tuberculosis, breast cancer, and lung opacity, etc. Furthermore, the challenge faced by the machine and deep learning experts these days is the unavailability of relevant data. Currently, the dataset adopted for the proposed  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Work** & **Method** & **Dataset** & **Date** & **Accuracy** \\ \hline \multirow{3}{*}{[PERSON] et al. [68]} & Inception-V3 with flattening, & \multirow{3}{*}{ \begin{tabular}{} \end{tabular}  } & Chest X-ray (COVID-19 and \\  & node dropping, normalization, & Pneumonia) 2020 & \\  & and dense layer & & \\ \cline{1-1} \cline{2-3} \cl  
approach contains only three types of images such as normal, COVID-19, and pneumonia. Therefore, to generalize the proposed CovidDetNet approach, we need to evaluate it with other types of chest radiograph disease images. For this purpose, in the future, we need to explore the existing benchmark dataset of chest radiographs containing breast cancer, tuberculosis, and lungs opacity together with the COVID-19 detection. This paper only focuses on the approaches that use deep learning to detect and identify COVID-19 from chest radiograph images. In the future, we are interested in conducting an experiment with expert radiologists and identifying the proposed approach's efficacy in identifying COVID-19.