# Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification
## 4 Result and Discussion
### Classification Results
#### 4.3.4 Comparison of the Proposed Methodology with Existing Techniques  
Table 6 compares and evaluates the classification results for the proposed methodology with other advanced methods. The ResNeXt with a WHO algorithm was utilized for classification purposes. The optimization strategy utilized in the suggested approach will increase classification accuracy while also enhancing algorithm performance. From Table 6, it is observed that the performance of Resnet-densenet-SCAM on the BCCD dataset was very low (88.44% accuracy) compared to other techniques. They did not use any segmentation technique. For feature extraction, the entire input image was used. As a result, undesirable traits were also extracted from the image. Moreover, the loss function used in this paper ignores the variations among the samples, which leads to poor performance. In the LISC dataset, the performance SVM was slightly decreased compared to that of other techniques. This was because the images in the LISC dataset contains a vast range of hue and intensity fluctuations. To tackle this problem, the pre-processing techniques were not implemented in [37] because it leads to misclassification. In the single-cell morphology dataset, four existing techniques were used for the comparison with the proposed approach. In that, the performance of the deep convolutional autoencoder (DCAE) and random forest was lower than the other techniques. Random forest technique contains many significant hyper parameters. The manual tuning of these hyper-parameters reduces the performance of the system. Moreover, the entire raw input image is processed in DCAE [22] for classification. They do not perform any segmentation \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Techniques** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{6}{*}{BCCD} & Resnet-densenet-SCAM [30] & 88.44 & 90.84 & 88.45 & 88.73 \\  & WBC-AMNet [31] & 89.22 & 90.72 & 89.22 & 89.47 \\  & Cubic SVM [32] & 98.44 & - & - & - \\  & DRETA-Net [33] & 95.87 & 96.13 & 92.94 & 94.51 \\  & Proposed & 99.24 & 99.48 & 99.33 & 99.4 \\ \hline \multirow{6}{*}{LISC} & K-nearest neighbor [34] & 97.8 & - & - & - \\  & Alexnet [35] & 97.21 & 72.75 & 89.6 & - \\  & CNN [36] & - & 93.42 & 96.27 & 94.73 \\  & SVM [37] & 92.21 & 92.65 & 92.44 & 92.44 \\  & Proposed & 99.16 & 99.28 & 99.19 & 99.22 \\ \hline \multirow{6}{*}{Single-cell morphology dataset} & deep convolutional & \multirow{2}{*}{93.12} & \multirow{2}{*}{67.35} & \multirow{2}{*}{81.65} & \multirow{2}{*}{-} \\  & autoencoder (DCAE) [22] & & & & \\ \cline{1-1}  & XGBoost [38] & 97.57 & 98.48 & 97.16 & 97.82 \\ \cline{1-1}  & Random forest [24] & 93.4 & 86.68 & 92.26 & - \\ \cline{1-1}  & Deep convolutional Neural & \multirow{2}{*}{98.27} & \multirow{2}{*}{96.95} & \multirow{2}{*}{98.04} & \multirow{2}{*}{-} \\ \cline{1-1}  & network (DCNN) [39] & & & & \\ \cline{1-1}  & Proposed & 98.61 & 98.66 & 98.49 & 98.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed approach with existing techniques on three datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**WBC Subtypes** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Erythroblast & 99.62 & 99.73 & 98.67 & 99.2 \\ Metamyelocytre & 98.15 & 98.23 & 98.2 & 98.21 \\ Monoblast & 98.45 & 98.49 & 98.47 & 98.48 \\ Myeloblast & 99.51 & 99.56 & 99.32 & 99.44 \\ Myelocyte & 98.35 & 98.38 & 98.47 & 98.42 \\ bilobled & 97.99 & 97.98 & 98 & 98 \\ Promyelocytre & 98.21 & 98.26 & 98.33 & 98.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Multi-class classification on single-cell morphology dataset. process. As a result, undesirable traits are also extracted from the image, which slows down the performance of the classifier. Compared to all the other techniques, the proposed method produced the best results for the datasets for BCCD, LISC, and single-cell morphology with 99.24%, 99.16%, and 98.57% accuracy, respectively. The following are reasons for the greater accuracy of the proposed method:  
1.