## 6 Results and Discussion
### Confusion Matrix  
In machine learning, the confusion matrix is a certain tabular representation that shows a classification algorithm's performance. This offers a more comprehensive analysis compared to accuracy. Each column of the matrix signifies the patterns in the expected data class whereas every row specifies the patterns in the obvious class. Table 4 below shows a confusion matrix using a two-class classifier with the subsequent cells as: \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{2}{c}{**Predicted Class**} \\  & **Positive** & **Negative** \\ \hline \multirow{2}{*}{**Actual Class**} & **positive** & _TP_ & _FP_ \\  & **negative** & _FN_ & _TN_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A confusion matrix for two-class classifier. Figure 9: Comparison among VGG-19, ResNet-50, and Inception-V3 based on kappa statistic values for (**a**) dataset 1, (**b**) dataset 2, and (**c**) dataset 3. True-Positive (_TP_) indicates the number of 'positive' examples classified as 'positive.' False-Positive (_FP_) implies the number of 'negative' examples classified as 'positive.' False-Negative (_FN_) denotes the number of 'positive' examples classified as 'negative.' True-Negative (_TN_) means the number of 'negative' examples classified as 'negative.' A two-class confusion matrix identifies several standard terms. The accuracy (i.e., classification accuracy) is the sum of the correctly classified samples divided by the total number of samples present. The following equation calculates this as:  
\[Accuracy=\frac{TP+TN}{TP+T+N+FP+FN} \tag{7}\]  
The precision denotes the ratio of the predicted positive samples discovered to be correct, as calculated using the following equation:  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
The _FP-Rate_ (i.e., False-Positive Rate) indicates the ratio of negative examples incorrectly classified as positive, as given by the equation:  
\[FP-Rate=\frac{FP}{FP+TN} \tag{9}\]  
The _TP-Rate_ (i.e., True-Positive Rate) or recall is the ratio of positive samples discovered correctly, as assessed using the equation:  
\[TP-Rate=Recall=\frac{TP}{TP+FN} \tag{10}\]  
A large precision value is occasionally more applicable while sometimes a large recall value is more obvious. However, in the most presentations, the objective is to increase both values. The cohesive arrangement of these values is the f-measure and usually formulated as a harmonic mean of both the values:  
\[F-measure=\frac{2*Precision*Recall}{Precision+Recall} \tag{11}\]  
Tables 5-7 display performances with respect to the aforementioned diverse metrics for datasets 1, 2, and 3, respectively. For evaluating the performance of  
This section explores comparison between the state-of-the-art methods and the proposed framework in accordance with F-measure for the three imagery benchmark datasets employed in this work. Several previous research works have emphasized accuracy as the most accurate paradigm for comparative study and the accuracy level is not only a sufficient and reliable performance measurement tool. Though such current research articles provide enhanced results, as depicted in Figure 11, proposed research work has acquired improvements to their results for the same datasets. Instead of accuracy measurement, the research work proposed in this paper highlights the F-measure as a strong and appropriate performance comparing tool. Figure 11 reveals the superior performance of the proposed framework which possesses better F-measure values 98.86%, 98.84%, and 99.19% for dataset 1 and dataset 2 to some current research techniques employing the aforesaid three imagery benchmark datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Model**} & \multicolumn{5}{c}{**70\%–30\% Distribution**} & \multicolumn{5}{c}{**10-Fold Cross-Validation**} \\ \cline{2-10}  & \begin{tabular}{c} **TP-** \\ **Rate/Recall** \\ **(\%) (sd)** \\ \end{tabular}  & \begin{tabular}{c} **TP-** \\ **Precision** \\ **(\%) (sd)** \\ \end{tabular}  &