## 4 Results and Discussions  
With the use of the two best datasets, the CNN and the activation of CNN provides machine learning techniques with fivefold and tenfold validation techniques included in the proposed work. We show the result of the proposed work in the form of tables and graphs where the training set is utilized to prepare the model, while the validation set is simply used to assess the model's presentation. We made a table of CNN activation for the tenfold technique and calculated the individual accuracy of individual classes. In Tables 3 and 4, detailed CNN layers and training parameters are shown. CNN validation accuracy as shown in Figure 4 remains saturated with an 87.89% validation accuracy. The results of CNN testing data are further discussed for each class that increase the testing accuracy as compared to validation accuracy. Furthermore, other important evaluation measure are also use and shown in Table 5.  
Although the validation accuracy was lower when testing data were collected at a 70:15:15 ratio: 70% data is used as training data, 15% as validation, and 15% as testing data, the overall results of classification are improved. The F1-score is an important measure \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** & **Precision** & **Kappa** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \hline \multirow{2}{*}{Atelectasis} & 0.96667 & 0.96667 & 0.99524 & 0.95082 & 0.93548 & 0.86492 \\ \hline \hline \multirow{2}{*}{Cardiomegaly} & 1 & 1 & 1 & 1 & 1 & 0.86667 \\ \hline \hline \multirow{2}{*}{Consolidation} & 1 & 1 & 1 & 1 & 1 & 0.86667 \\ \hline \hline \multirow{2}{*}{Covid-19} & 0.93333 & 0.93333 & 0.99524 & 0.93333 & 0.93333 & 0.8673 \\ \hline \hline \multirow{2}{*}{Edema} & 1 & 1 & 1 & 1 & 1 & 0.86667 \\ \hline \hline \multirow{2}{*}{Effusion} & 0.9 & 0.9 & 0.99286 & 0.9 & 0.9 & 0.86761 \\ \hline \hline \multirow{2}{*}{Emphysema} & 0.96667 & 0.96667 & 0.99762 & 0.96667 & 0.96667 & 0.86698 \\ \hline \hline \multirow{2}{*}{Fibrosis} & 0.96667 & 0.96667 & 1 & 0.98305 & 1 & 0.86904 \\ \hline \hline \multirow{2}{*}{Hernia} & 1 & 1 & 1 & 1 & 1 & 0.86667 \\ \hline \hline \multirow{2}{*}{Infiltration} & 0.96667 & 0.96667 & 0.99762 & 0.96667 & 0.96667 & 0.86698 \\ \hline \hline \multirow{2}{*}{Mass} & 1 & 1 & 0.99762 & 0.98361 & 0.96774 & 0.8646 \\ \hline \hline \multirow{2}{*}{Nodule} & 0.93333 & 0.93333 & 0.99762 & 0.94915 & 0.96552 & 0.86935 \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Pleural \\ Thickening \\ \end{tabular} } & 1 & 1 & 0.99762 & 0.98361 & 0.96774 & 0.8646 \\ \hline \hline \multirow{2}{*}{Pneumomia} & 0.96667 & 0.96667 & 1 & 0.98305 & 1 & 0.86904 \\ \hline \hline \multirow{2}{*}{Pneumothorax} & 0.9 & 0.9 & 0.99286 & 0.9 & 0.9 & 0.86761 \\ \hline \hline \multirow{2}{*}{Overall} & 0.9667 & 0.9667 & 0.9976 & 0.9667 & 0.9669 & 0.7321 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: CNN testing data results. of evaluation as it contains the effect of precision and recall. The precision is the ratio of true positives over true positives and false positives. The 96.67% value is a good measure of predictivity. Similarly, a kappa value of more than 60% shows the moderate level of agreement on testing data. However, to increase the overall evaluation measure performance, the transfer learning based deep features are used and tested via classical machine learning methods that improved the accuracy and other results as well. Although the CNN testing data results were good enough, to increase accuracy, the transfer learning concept was used. Therefore, to improve the testing data results, deep transfer learning is utilized using self-activation on the proposed trained fully connected CNN layers. These features are embedded within classical ML classification methods. The best classification results selected among 23 different methods are shown in Tables 6 and 7 with fivefold and tenfold validation methods.