## 4 Results and Discussion  
This research is a proof of concept to bring out the possibility of training a CNN network-based classifier by using training images which have only few pixels that are retained and remaining forced to zero which facilitates the reduction of information to be communicated in an application platform. This is based on the fact that the retained pixels can be assumed as the CS measurements and can be used directly in the training process. This CS process can be easily implemented practically with simple digital cameras. By looking at the result of image obtained by retaining only the 50% of the pixels shown in Figure 2b, we can infer that it is legible for human eye to classify it as a cat and same thing applicable for Figure 2c-e. Intuitively we can say that the deep CNN which mimics human visual system can also recognize the image from the CS images having partial amount of pixels. We also consider the dataset that has natural images rather than simple images and it is interesting to note the result of our classification experiment in Table 1, that for the case of CS testing images the training accuracy of CNN models trained with CS images are  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Percentage of Pixels Retained**} & \multirow{2}{*}{**Training Accuracy**} & \multirow{2}{*}{**Validation Accuracy**} \\ \cline{2-2} \cline{4-5}  & **Training Dataset** & & & \\ \hline Without GA & 10\% & 10\% & 67\% & 45\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training and validation accuracy of the CNN on multiclass images without GA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Percentage of Pixels Retained**} & \multirow{2}{*}{**Training Accuracy**} & \multirow{2}{*}{**Validation Accuracy**} \\ \cline{2-2} \cline{4-5}  & **Training Dataset** & & & \\ \hline Crossover & 10\% & 10\% & 50\% & 44\% \\ Diagonal Crossover & 10\% & 10\% & 67\% & 52\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training and validation accuracy of the CNN on multiclass images with GA.  
Figure 9: The validation and training performance with respect to epochs for multiclass (5) CNN model achieved through GACL for P = Q = 10%, (**a**) accuracy with vertical crossover, (**b**) loss with vertical crossover, (**c**) with diagonal crossover, (**d**) loss with diagonal crossover.  
better than that of training with complete images. The percentage of pixels not retained can be considered as the CR and for the case of CR of 90% the training accuracy is 77% with the validation accuracy 61%. In this case the CNN used is a very basic architecture which is vastly used for common image recognition application. For the completeness of the experiment all the model accuracies graphs are presented in Figure 3a-e. To bring out the efficacy of the proposed GACL algorithm, the results obtained by using the binary mask learned by GA is given in Table 3 and can be observed that the training accuracy of 77% is improved to 85% using diagonal crossover for the case of 90% CR and the result is supported with all model accuracy graphs Figure 7a-d. So here we validate the performance of the novel GACL method which could be integrated with any other good performing CNN.