# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 5 Discussion
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Dataset**} \\ \hline Actual & COVID-19 & Lung Opacity & Normal & Pneumonia & Lung Opacity & COVID-19 & Lung Opacity \\ ResNet-50 & COVID-19 & COVID-19 & Normal & Pneumonia & Lung Opacity & Pneumonia & Normal \\ ResNet-101 & COVID-19 & Lung Opacity & Normal & Pneumonia & Lung Opacity & COVID-19 & Normal \\ ResNet-152 & COVID-19 & Lung Opacity & Normal & Pneumonia & Lung Opacity & COVID-19 & Lung Opacity \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._large opacity refers to opacities with dimensions greater than 10 mm. This distinction between the two types of opacities introduces complexity in classification, contributing to the observed difficulties in accurate identification. As a result, the models run the risk of misclassifying lung opacity and normal as having small opacity. The ResNet-152 architecture demonstrated powerful performance compared to the other architectures across various metrics, including recall, precision, and F1 score, as indicated by the results of the confusion matrix evaluation. This aligns with previous research findings that suggest higher accuracy can be achieved with deeper architectures [32]. The ResNet-152 model's significantly greater number of layers compared to other ResNet architectures allowed it the learning of more complex patterns and consequently achievement of improved performance. According to the analysis results, the time required for one epoch of training was 461 s for ResNet-152, 311 s for ResNet-50, and 549 s for ResNet-101. This discrepancy in training times is attributed to the fact that as the number of layers in an architecture increases, so does the number of parameters used. ResNet-152 boosted 58 million parameters, while ResNet-50 had 23 million, and ResNet-101 had 42 million. Consequently, training times were extended. Therefore, the choice of architecture should be aligned with the specific requirements of the research or use case. While ResNet-152 offered superior performance, it came at the cost of increased computational demands, so researchers should consider their computational resources and objectives when selecting the most appropriate architecture. Deployment, as a crucial step in facilitating the diagnosis of lung problems in patients by medical personnel, involves the creation of an application-based model for enhanced usability. This process entails the necessity of retraining the model to prevent any potential decline in performance, which could have critical consequences. Moreover, ongoing validation and supervision by radiologists ensures that the model maintains its accuracy over time. However, when considering the broader application of the model to generalize for all lung disease patients across diverse demographics, geographic locations, and healthcare service standards, it is evident that the current dataset remains limited and constrained. Consequently, there is a clear need for the acquisition of additional datasets. These new datasets are essential to train the model effectively, allowing it distinguishing between various types of data variance and ensuring its robust performance in diverse real-world scenarios. Expanding the dataset to encompass a wider range of cases and demographics enhances the model's ability to provide accurate diagnoses and predictions for a more diverse patient population.