# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
## 5 Results
### Validation of Classification of EfficientNet-B7-Based CNN for Step-2-2  
For the fundus images identified as Class B in the previous step, EfficientNet-B7-based CNN identifies the individual classes as either Class 1 or Class 2.  
Figure 19 shows a confusion matrix and AUC score and ROC curve from the validation of the classification by EfficientNet-B7-based CNN in Step-2-2. In the confusion matrix, the classification accuracy for Classes 3 and 4 is about 44% and 48%, respectively, so the overall classification accuracy is 92%.  
In Table 2, the optic disc and macula of the fundus images in Class 3 look clean and have no pigment abnormality or bleeding. On the other hand, the fundus images in Class 3 are an overall blurry image compared to Class 4. In the misclassification case of Class 3, the fundus images, as shown in Figure 20a, have the optic disc and macula in clearer. Meanwhile, in the misclassification case of Class 4, the optic disc and macula in the fundus image are not clearly observed, and a lot of blurred images are observed.  
The conclusion about the classification accuracy of the 4-Class VA Classifier using fundus images is as follows: We randomly selected 1000 for each class and tested the classifier with a total of 4000 fundus images and the relevant patient's VA information. Figure 21 is a confusion matrix for the classification accuracy of the entire model based on the ensemble method. It combines Figures 14a, 17a and 19a and summarizes the classification performance of the 4-Class VA classifier. This confusion matrix consists of two types of quadrants: big quadrants and small quadrants. The big quadrants include the classification accuracy rate for Classes A and B performed at Step-1. The small quadrants  
Figure 19: Validation results of EfficientNet-B7-based CNN classification for Step-2-2. (**a**) Confusion matrix; (**b**) AUC (ROC) report.  
Figure 18: Examples of misclassification of Classes 1 and 2 by the SVM-RBF-Kernel. (**a**) Class 1; (**b**) Class 2.  
include the classification accuracy rate for Classes 1-4 performed at Step-2. Notice the numbers on the diagonal in this confusion matrix, starting at the top left and flowing down to the right. These numbers are the classification accuracy for 1000 fundus images from each class from Class 1 to Class 4. It says that the classification accuracy of our approach to VA measurement based on fundus images are 88.5%, 58.8%, 88.0% and 94.3% for each classification of Class 1 to Class 4, respectively. We can say that the classification accuracy of the 4-Class VA classifier is 82.4% on average.  
Table 7 shows the comparison between the performance of VA classifiers based on our ensemble method and VGG-19 in terms of four aspects: the overall average accuracy, each class accuracy, sensitivity, and specificity. The reason why VGG-19 is selected to compare against our ensemble method is that it shows the best performance of VA classification as shown in Table 5. It shows that our ensemble method outperforms the VGG-19 VA classifier in the overall accuracy, but the VGG-19 VA classifier shows higher accuracy in VA- classification for Class-2 than our ensemble method. From the aspects of sensitivity and specificity, they are not comparable because one of