## 5 Experimental Results
### Comparison with Studies in the Literature  
In this study, we proposed a new feature selection optimization technique and analyzed its performance with four different datasets. The proposed optimizer helped all machine learning models to improve their performance and their speed. As shown in Figure 6, the proposed optimizer selected the smallest number of features from all datasets, except for the Chest dataset. These features were the most informative features because they supported the machine learning algorithms in achieving the best results, as shown in Figure 7. In addition, the trained classifiers were faster based on the selected features of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Alg.** & **Model** & **Accuracy** & **BA** & **F1-Score** & **Recall** & **Precision** & **Time** & **No. of Features** \\ \hline \multirow{4}{*}{PSO} & DT & 0.8733 & 0.8733 & 0.8764 & 0.8733 & 0.8978 & 2.0821 & \multirow{4}{*}{347} \\  & LDA & 0.8838 & 0.8837 & 0.8859 & 0.8838 & 0.9053 & 1.2637 & \\  & SVM & 0.8858 & 0.8858 & 0.8877 & 0.8858 & **0.906** & 0.8885 & \\ \hline \multirow{4}{*}{MFO} & DT & 0.881 & 0.8809 & 0.8824 & 0.881 & 0.8976 & 1.5234 & \multirow{4}{*}{225} \\  & LDA & 0.8814 & 0.8813 & 0.8834 & 0.8814 & 0.9031 & 0.738 & \\  & SVM & 0.8838 & 0.8838 & 0.8859 & 0.8838 & 0.9055 & 0.7944 & \\ \hline \multirow{4}{*}{WOA} & DT & 0.8778 & 0.8777 & 0.88 & 0.8778 & 0.9005 & 1.4109 & \multirow{4}{*}{226} \\  & LDA & 0.8806 & 0.8805 & 0.8828 & 0.8806 & 0.9027 & 0.7313 & \\  & SVM & 0.8838 & 0.8838 & 0.8856 & 0.8838 & 0.9041 & 0.7249 & \\ \hline \multirow{4}{*}{AO} & DT & 0.8806 & 0.8805 & 0.8822 & 0.8806 & 0.898 & 0.8553 & \multirow{4}{*}{125} \\  & LDA & 0.879 & 0.8789 & 0.8811 & 0.879 & 0.9014 & 0.5041 & \\ \cline{1-1}  & SVM & 0.8838 & 0.8838 & 0.886 & 0.8838 & 0.9058 & 0.6661 & \\ \hline \multirow{4}{*}{AHA-AO} & DT & 0.8721 & 0.8721 & 0.8753 & 0.8721 & 0.8977 & 0.8567 & \multirow{4}{*}{132} \\  & LDA & 0.8818 & 0.8817 & 0.884 & 0.8818 & 0.9037 & 0.4571 & \\ \cline{1-1}  & SVM & 0.8846 & 0.8846 & 0.8863 & 0.8846 & 0.9035 & 0.53 & \\ \hline \multirow{4}{*}{AHA-AO} & DT & 0.8749 & 0.8749 & 0.877 & 0.8749 & 0.8956 & 0.4053 & \multirow{4}{*}{65} \\  & LDA & 0.8826 & 0.8825 & 0.8844 & 0.8826 & 0.903 & **0.1887** & \\ \cline{1-1}  & SVM & **0.8862** & **0.8862** & **0.8878** & **0.8862** & 0.9053 & 0.2579 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification results of each feature selection optimization algorithm on the Blood-Cell dataset (bold refers to best value). the AHA-AO algorithm, as shown in Figure 8. To sum up, using the proposed AHA-AO as a a feature selection technique in the machine learning pipeline has been proven to (1) allow the selection of the best feature set, (2) improve the performance of the model, and (3) speed up the learning process. This section compares the proposed approach with the state-of-the-art medical image classification techniques in the literature. Figure 6: Numbers of selected features of different optimizers for all datasets. Figure 7: Comparison among the best classifiers with every optimizer and the four datasets: (**A**) for the ISIC-2016 dataset, (**B**) for the PH2 dataset, (**C**) for the Chest dataset, and (**D**) for the Blood dataset. Table 8 shows the results of some important methods. For a fair comparison, we concentrate on studies that used the same datasets. For the ISIC-2016 dataset, the following advanced skin cancer identification methods were compared: a method based on segregation and then validation [34]; a method that relied on feature fusion [32]; a method correlated with Fisher coding and deep residual networks [39]; a multi-CNN interactive learning model [40]; an ensemble method [59]; an integration of a Fisher vector and CNN fusion [41]. [PERSON] et al. [34] proposed a fully convolutional residual network (FCRN) for segmentation, and further enhanced it by adding a multi-scale contextual information strategy to expand its capabilities.