## 4 Results and Discussion  
Table 1 presents the results using VGG16 features. It shows the accuracy of each classification algorithm along with several performance evaluation measures, such as precision, recall (sensitivity), f1-score, and training time. Precision (also known as positive predictive value) represents a fraction of the relevant parasitic blood-smear images among the retrieved instances. Precision is mathematically represented as \(\frac{TP}{TP+FP}\), where TP stands for true positive, and FP stands for false positive. Recall represents a fraction of relevant retrieved instances from parasitic blood-smear image samples. Recall is mathematically represented as \(\frac{TP}{TP+FN}\), where FN stands for false negatives. F1-score is the harmonic mean of precision and recall. Figure 2: The feature extraction and training process. The result in Table 1 shows the performance outputs of all four classification algorithms. These results were obtained from the VGG16 features. DT and SVM achieved precision of 89.10% and 94.26%, recall of 89.11% and 95.57%, f1-score of 89.22% and 94.91%, and accuracy of 89.24% and 94.88%, respectively. The precision for NB and KNN classifiers is 62.83% and 88.66%, recall of 96.47% and 96.49%, f1-score of 76.10% and 92.41% and accuracy of 69.70% and 92.07%, respectively  
The result in Table 2 shows performance outputs of all the four classification algorithms obtained from the VGG19 features. DT and SVM achieved precision of 86.06% and 93.84%, recall of 85.82% and 95.22%, f1-score of 85.94% and 94.52%, and accuracy of 85.94% and 94.48%, respectively. The precision for NB and KNN classifiers are 60.26% and 85.71%, recall of 96.64% and 95.15%, f1-score of 74.23% and 90.19% and accuracy of 66.46% and 89.64%, respectively. The result in Table 3 shows performance outputs of all the four classification algorithms obtained from the ResNet50 features. DT and SVM achieved precision of 86.89% and 94.22%, recall of 87.17% and 95.53%, f1-score of 87.03% and 94.87%, and accuracy of 87.05% and 94.84%, respectively. The precision for NB and KNN classifiers are 80.82% and 84.69%, recall of 87.67% and 94.76%, f1-score of 84.11% and 89.44% and accuracy of 88.43% and 88.81%, respectively. The result in Table 4 shows performance outputs of all the four classification algorithms obtained from the ResNet101 features. DT and SVM achieved precision of 88.93% and 94.11%, recall of 88.40% and 95.60%, f1-score of 88.45% and 94.85%, and accuracy of 88.48% and 94.81%, respectively. The precision for NB and KNN classifiers are 82.95% and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & \multicolumn{5}{c}{**Performance Evaluation Metrics**} \\ \cline{2-6}
**Algorithms** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Time (s)** \\ \hline
**DT** & 0.8910 & 0.8933 & 0.8922 & 0.8924 & 3528.79 \\ \hline
**SVM** & 0.9426 & 0.9557 & 0.9491 & 0.9488 & 71,163.34 \\ \hline
**NB** & 0.6283 & 0.9647 & 0.7610 & 0.6970 & 23.01 \\
**KNN** & 0.8866 & 0.9649 & 0.9241 & 0.9207 & 9626.79 \\ \hline \end{tabular}
\end{table} 
Table 1: Classification results using VGG16 features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & \multicolumn{5}{c}{**Performance Evaluation Metrics**} \\ \cline{2-6}
**Algorithms** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Time (s)** \\ \hline
**DT** & 0.8606 & 0.8582 & 0.8594 & 0.8594 & 2375.60 \\ \hline
**SVM** & 0.9384 & 0.9522 & 0.9452 & 0.9448 & 80,246.96 \\ \hline
**NB** & 0.6026 & 0.9664 & 0.7423 & 0.6646 & 23.64 \\ \hline
**KNN** & 0.8571 & 0.9515 & 0.9019 & 0.8964 & 9635.20 \\ \hline \end{tabular}
\end{table} 
Table 2: Classification results using VGG19 features.