## 5 Comparative Results
[4]} & ALL-IDB 1 108 & SVM & 93.7\% & & & \\ \cline{3-6}  & & ISPI2019 & DCNN & 96.6\% & 96.9\% & 91.8\% & 94.7\% \\ \hline \multirow{3}{*}{[PERSON] et al. [9]} & ISBI & DCNN & 96.17\% & & 95.2\% & & 98.6\% \\ \cline{3-6}  & & CNN & 88.3\% & & & \\ \cline{3-6}  & & SVM & 50.1\% & & & \\ \cline{3-6}  & & NB & 69.7\% & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [11]} & ALL-IDB2 & \begin{tabular}{c} AlexNet+GoogleNet+ \\ SqueezeNet+SVM \\ \end{tabular}  & 98.2\% & 99.2\% & 98.1\% & 96.3\% & 99.1\% \\ \cline{3-6}  & & RF & **99.7\%** & **99.5\%** & **99.5\%** & 99\% \\ \cline{3-6}  & & SVM & 98.8\% & 97.5\% & 96\% & 96.5\% & 93.1\% \\ \cline{3-6}  & overall & KNN & 98.6\% & 97.5\% & 97\% & 97.5\% & 95.1\% \\ \cline{3-6}  & & NB & 92.8\% & 88\% & 86\% & 87\% & 76.2\[PERSON] et al. 's [7] proposed approach was able to fuse features extracted from the best deep learning models and outperformed individual networks with an accuracy of 96.6%, precision of 96.9%, sensitivity of 91.75%, and F1-score of 94.7% in Leukemic B-lymphoblast diagnosis. When comparing these results with our proposed methodology, we found that the results of our suggestions when using RF were higher in accuracy, sensitivity, and specificity as shown in Table 6.  
[PERSON] et al. [9] presented an automatic CNN hybrid method for the classification of ALL and healthy cells with an accuracy of 96.2%, sensitivity of 95.2%, and specificity of 98.6%. When comparing these results with our proposed methodology, we found that the results of our suggestions when using RF were higher in accuracy, sensitivity, and specificity as shown in Table 6.  
The ALL-DB dataset was used by [PERSON] et al. [10]. When the classifier CNN was used in this study, the highest accuracy was 88.3%. The accuracy achieved by this study was 50.1% when using SVM and 69.7% when using NB. The proposed model employed an SVM classifier that was superior to that used by [PERSON] et al. [10], with an overall accuracy of 98.8%; additionally, the proposed model employed an NB classifier with an overall accuracy of 92.8%, which means the classifier's ability to correctly classify the class label was higher than that of [PERSON] et al. [10]. [PERSON] et al. [11] proposed work to suggest principal features for the detection of ALL. The highest accuracy achieved by this study when using (AlexNet+GoogleNet+SqueezeNet +SVM) was 98.2%, precision of 99.2%, recall of 98.1%, F1-score of 96.3%, and specificity of 99.1%. This means that [PERSON] et al. 's [11] proposed work had the ability to correctly identify people who did not have the disease by 0.1% from our proposed methodology. However, compared with the proposed methodology, it was found that the accuracy is increased by 1.5%, which means that the classifier's ability to correctly classify the naming of the class was higher. Moreover, it had a higher number of correct positive categories with a percentage of 0.3% and was higher in calculating the number of correct positive classes made out of all positive classes classification with a percentage of 1  
using image processing techniques. The dataset used included 3189 PBS images from 89 patients suspected of having ALL, whose blood samples were organized and stained by highly skilled laboratory personnel. This dataset was split into two categories: benign and malignant. Firstly, the image was enhanced by using stretchclim and adaptive thresholding. Secondly, extracting features was carried out by using feature extraction methods. Thirdly, the number of features was reduced by using the grey wolf optimization algorithm. Finally, acute lymphoblastic leukemia was classified into benign and malignant using RF, SVM, KNN, and naive Bayes classifiers. After employing the grey wolf optimization technique in the feature selection process, it was demonstrated that it provides good efficiency in all cases. The proposed work obtained the highest accuracy of 99.69%, the highest sensitivity of 99.5%, the highest precision of 99.5%, F1-score of 99.5%, and specificity of 99%, proving the efficacy of these methods.