## 3 Results
### Proposed System Results  
It is noted that the testing accuracy is limited despite the high accuracy through the training phase, which indicates overfitting in the training phase. To mitigate the effect of this drawback, we used the technique of dropping out a portion of the fully connected network. Many experiments have been done to deduce the optimal ratio of the drop out portion, and it is found to be 40%. It is noted that this value is critical, and the validation accuracy is very sensitive to this value. This technique has improved the testing accuracy by at least 6%. As a comparison, the results of the classification accuracy, sensitivity, and specificity of the trained models are reported in Table 1. The table contains both fused and original gray image dataset classification results. The performance of DRNet showed an acceptable performance compared to the other models, especially in the fused images dataset. As a first trial in the experiments, we divided each dataset ten-fold, taking into account that both eye images of the same person are either in the training folds only or in the testing fold only to avoid overfitting. Data augmentation and transfer learning are also used to overcome the possible overfitting. The data are augmented by horizontal flipping and random rotation, which results in an increase of data size to 800 images. After that, we applied DRNet, as well as other models, separately. The classification metrics are computed for each fold and then averaged. The gray-level image results and the comparison with the fused images are shown in Table 1. These results are the average percentage and the standard deviation (STD) of many experiments. It is clear from Table 1 that the STDs in case of gray images, especially in the case of the AlexNet results, are very large, even after 1000 epochs of training, which is an indicator of the instability of the performance of AlexNet in the gray-level dataset. One advantage of DRNet is the stability of performance despite the low accuracy in the case of gray-level images compared to that of AlexNet. On the other hand, using our proposed fused-image dataset has greatly increased the accuracy and other metrics as well as reduced the STD in using either DRNet or other models, as shown in Table 1. It is clear that the accuracy is increased by an average of about 20%, and the STD is decreased by about one-fifth in the results of AlexNet and DRNet. Moreover, sensitivity and specificity are improved by an average of 16%. Due to the limited number of images, it is suggested to use leave-one-subject-out (LOSO) in the training phase to adjust more realistic representative results. The results are shown in Table 2 for the four used CNN models. It is clear from these results that the performance of the DRNet is very close to that of the AlexNet. On the other hand, the results show the superior performance of the proposed fused images technique in all networks. In the training phase, AlexNet takes about double or triple the number of training epochsmore than that required for DRNet. Moreover, it takes also about double the time required to complete a single epoch, which in turn means that the required time to complete one training phase of AlexNet is about five times or greater than its corresponding time in the case of DRNet. The transfer learning-based networks converge at a lower number of training epochs but with a longer time for each individual epoch. The comparison and performance evaluation of the different used CNN models can be investigated using the ROC curves and the area under the curve (AUC). Each model is evaluated separately on the fused-images dataset and the results are shown in (Figure 8). From this figure, we can conclude that AlexNet and Densenet121 have the best performance  
From the above results, we can conclude the merit of using fused images in improving the classification capability of the CNN compared to the usual original images.