# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 5 Discussion
The image distribution for the former was 400 each for COVID-19 and pneumonia cases and, for the latter, it was 2911 images divided into 237 COVID-19 positive images, 1338 normal images, and 1336 viral pneumonia images. The current work utilized the Kaggle database, which contains 3616 COVID-19 chest X-ray images, and which is about 426% the size of the maximum number of COVID-19 images utilized in the covered literature. For the comparison with previous research work, the authors used the given hyperparameters and the same extended architecture employed in a previous study using MobileVNet2 as a base model. The previous metrics evaluations were 99.658% for accuracy, and 1 for precision, recall, and F1 score from the literature work. From the analysis of the current dataset, the values for the same architecture were 93.45% for accuracy, 94% for precision, and 95% for both recall and F1 score. Drops of 5% and 6% were observed in the MobileNetV2 model during the subject evaluation, where accuracy and precision values dropped by approximately 6%, while a curtail of 5% was observed for recall and the F1 score. The Table 3 is shown the comparison of previous state-of the-art studies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Studies**} & \multirow{2}{*}{**Cases Number X-ray Datasets**} & \multirow{2}{*}{**Method Utilized**} & \multirow{2}{*}{\
\begin{tabular}{c} **Accuracy (\%) Binary Classification** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy (\%)** \\ **Multi-Class Classification** \\ \end{tabular} } \\ \cline{1-1} \cline{5-5}  & COVID-19 & Pneumonia & Normal & & \\ \hline
[23] & 237 & 1336 & 1338 &
\begin{tabular}{c} Extended \\ MobileNetV2 architecture \\ \end{tabular} & N/A & 99.66\% \\ \hline
[26] & 400 & 400 & COVID-CheXNet architecture & 99.90\% & \\ \hline
[25] & 415 & 5179 & 2880 & VGG16 CNN & 93.9 \\ \hline
[29] & 850 & 500 & 915 & \
\begin{tabular}{c} Inception ResNetV2, \\ InceptionNetV3 and \\ NASNetLarge \\ \end{tabular} & N/A &
\begin{tabular}{c} 97.87\%, 97.87\% \\ and 96.24\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: State-of-the-art comparison COVID-19 studies. Another comparison from a literature study where CoroNet, a deep learning architecture comprising Xception as proposed. The model achieved 94.59% accuracy, 95% precision, 96.9% recall, and 95.6% F1 score on a 3-class classification. The evaluation on another dataset in the same study demonstrated an accuracy of 90.21%, precision of 97%, recall of 89%, and an F1 score of 93%, with the dataset composition comprising 157, 500, and 500 chest X-ray images for COVID-19, normal, and pneumonia, respectively. The current study used an Xception-based model with a global average pooling layer and a classification layer (Softmax), and showed 94.21% accuracy, 99% precision, 95% recall, and a 97% F1 score. The comparison showed an increase of 4% in precision, while other values were competitive. An overall increase of 4% to 6% was observed for the Xception model when compared with the lower values of metrics from the above-mentioned literature work. The summary of the literature research work and the chest X-ray image distribution for each class in the dataset is shown in Table 1. The highest accuracy achieved for COVID-19 detection using lung X-ray images is 99.658% for 3-class classification. However, the previous studies had limitations in that a high imbalance in class was observed and further, contained very few COVID-19 chest X-ray images. As already mentioned, the highest number of images for COVID-19 used was 850, whereas the number of COVID-19 samples in this research was about 426% of this number (3616 images). Therefore, the previous work limited in terms of the low number of COVID-19 cases in the dataset and the class imbalance. With this dataset, the authors achieved a maximum accuracy of 94.21%, which is greater than the accuracy achieved for the Xception-based model by 4%.