## 5 Comparative Results  
A comparative result between the proposed and the other techniques in the literature was performed. Additionally, we compared machine learning algorithms SVM, NB, KNN, and RF algorithms for the binary classification. Table 6 shows different study results. [PERSON] et al. [1] attempted to investigate deep CNN applications in which they developed pre-trained VGG-16 and ResNet for the diagnosis and classification of ALL and identified between healthy and cancer cases. They were able to achieve 81.6% ResNet, 84.6% VGG-16, 77.9% KNN, and 82.1% proposed CNN testing accuracy. These classifiers measured the overall precision, sensitivity, and F1-score, which are summarized in Table 6. The highest precision of 85% was obtained with VGG-16, the highest sensitivity of 83.5% with VGG-16, and the highest F1-score of 84% with VGG-16. While the proposed work was superior to that of [PERSON] et al. [1], it achieved the highest accuracy when a random forest classifier was used. It reached 99.7% accuracy in correctly classifying acute lymphoblastic leukemia cases based on the total number of test images. The prec  
Figure 16: Effect of grey wolf in reducing the number of features. Figure 15: The rate of increase in confusion matrices after using grey wolf optimization algorithm. [PERSON] et al. [2] sought to classify treatment outcomes for 241 ALL children and adolescents aged three months to seventeen at the time of diagnosis. [PERSON] et al. [2] used SVM with a cost parameter of 100 and outperformed it with an accuracy of 94.9%, precision of 90.2%, sensitivity of 86.2%, and F1-measure of 88.2%. They also used the RF classifier, which had an accuracy of 90.9%, a precision of 79.6%, a sensitivity of 92.2%, and an F1-measure of 85.4%. While the proposed work outperformed that of [PERSON] et al. [2], it achieved the highest accuracy when using a random forest classifier. This means that it reached 99.7% accuracy when calculating accuracy by dividing the number of correctly classified acute lymphoblastic leukemia cases by the total number of test images. The precision, which measures the number of correct positive classes, was 99.5%. The model had a specificity of 99% when comparing the true proportion of negative results to all actual negatives. The F1-score obtained indicates that the model was effective at identifying benign cases; it was 99.5%. When the proportion of true positives to all actual positives was calculated by the RF classifier, recall reached 99.5%. With an overall accuracy of 98.8%, the proposed model employed an SVM classifier that outperformed that used by [PERSON] et al. [2]. The accuracy was 98.8%. When the SVM classifier was used to calculate the true positive rate, sensitivity reached 96%. The obtained F1-score was 96.5%. The model had a specificity of 93.1% when measuring the true negative rate. [PERSON] et al. [4] made use of the Public ALL-IDB 1 (108 images). The highest accuracy achieved by this study when using SVM was 93.7%. The proposed work outperformed [PERSON] et al. [4]; the proposed model employed an SVM classifier that outperforms the one used in [4] with overall correctness of 98.8% which means the classifier's ability to correctly classify the class label was higher than that of [PERSON] et al. [4]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Dataset** & **Algorithm** & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** & **Specificity** \\ \hline \multirow{6}{*}{[PERSON] et al. [1]} & \multirow{3}{*}{12,528 images} & VGG-16 & 84.6\% & 85\% & 83.5\% & 84\% \\ \cline{3-6}  & & ResNet-50 & 81.6\% & 84\% & 82.5\% & 83.5\% \\ \cline{3-6}  & & KNN & 77.9\% & 75\% & 75\% & 75\% \\ \cline{3-6}  & & Proposed CNN & 82.1\% & 83.5\% & 81\% & 82\% \\ \hline \multirow{3}{*}{[PERSON] et al. [2]} & ALL 241 & SVM & 94.9\% & 90.2\% & 86.2\% & 88.2\% \\ \cline{3-6}  & patients & RF & 90.9\% & 79.6\% & 92.2\% & 85.4\% \\ \hline \multirow{3}{*}{[PERSON] et al.