## 4 Results
### Discussion and Comparison  
In this section, we will be discussing the results of our proposed methodology with the past work. After acquisition of images, we performed pre-processing, which was used to enhance the image quality, removed noise and sharpen of image dataset. Moreover, in pre-processing we have also achieved the segmentation. No special model was trained to segment the image dataset. Segmentation itself has been performed in pre-processing. This helps us by reducing computation required for image dataset segmentation, and also involves reduced computation time. In the next step, we trained two models of CNN that work in parallel to extract features from the dataset. After performing activation on extracted vectors of feature, which was the output of each CNNs, we performed CCA fusion to extract and concatenate the most promising featured vector, which is also our contribution. We also have compared CCA fusion with Principal Component Analysis (PCA) and Serial Based Approach (SBA). Lastly, we use traditional machine learning models to train and test. The reason for using a traditional machine learning model was to minimise the computation of our network. Traditional machine learning models take a few minutes to train, but if we use a deep learning network in this step, it might take several hours or even few days. By using traditional machine learning algorithms, we observed that the best one on our proposed was Bagging ensemble model with CCA fusion. This gives us an accuracy of 97.04 percent overall. However, SVM and Coarse KNN gives us the worst result among used classifiers in our proposed methodology. The accuracy observed by both SVM and Coarse KNN was around 95%, which is not particularly bad. Figure 14: Confusion Matrix of RUSBoost model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Statistics** & **Results \%** \\ \hline Accuracy & 96.30 \\ \hline Error & 3.70 \\ \hline Sensitivity & 96.23 \\ \hline Specificity & 98.15 \\ \hline Precision & 96.41 \\ \hline False Positive Rate & 1.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Overall Statistics of RUSBoost of All Classes. Furthermore, comparison between CCA, PCA, and SBA was also performed to validate the comparative analysis between them. According to the experimental results performed on CCA, PCA, and SBA. LPBoost performed best by using SBA fusion technique with the accuracy of 96.67%. While using PCA fusing technique, medium KNN and RUSBoost maintained the accuracy of 96.67%. We concluded that CCA fusion produced the best results compared to feature concatenation techniques. The Bagging Ensemble Model computed an accuracy of 97.04% with CCA fusion. Moreover, the statistics of CCA, PCA, and SBA on traditional machine learning models are listed below in Table 26.  
At the end, we discuss the statistics of our proposed methodology with the previous work. Our proposed methodology has improved the results significantly. An author [21] used ANN along with SVM to classify Acute Lymphoblastic Leukaemia (ALL). Their specificity was 95.31%. Another scientist used CNN and SVM to identify the leukaemia. Their dataset consists of only two types of leukaemia. They achieved the accuracy of 94.1% [22]. The authors of [6] proposed an active contours algorithm to identify the Leukocytes with accuracy of 96.5%. Lastly, we compared the adopted methods with the state of the art model in [11], which is an eight-layered a pre-trained convolutional neural network from the ImageNet database that has been trained on over a million photos. The network has been pre-trained to categorise photos into 1000 different item categories. Their methodology found an accuracy of 90.30%. The results are cited below in Table 27.  
Abbreviations used in this study are give in Table 28.