# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
## 4 Deep Learning-Based Ensemble Method for VA Measurement
### ML Models and Classification Performances
#### 4.2.3 ML Model for Step-2-2: EfficientNet-B7  
EfficientNet is a state-of-the-art model with the best performance with a few parameters about image classification. The scaling-up method is often utilized to improve the performance of CNN. For scaling-up, one can increase the number of layers, the number of channels, or the input image's resolution. EfficientNet finds the optimal combination of the above three scaling-up methods through AutoML (Automated Machine Learning) by uniformly adjusting the three methods using compound coefficients.  
EfficientNet is composed of MBConv structured as shown in Figure 11. The MBConv expands the channel through 1 \(\times\) 1 convolution operation and performs a Depthwise convolution operation that performs a convolution operation on each image channel. Depthwise convolution performs a convolution operation with \(k\times k\) kernel for each channel of images. Each channel operated by Depthwise convolution becomes a feature map. Each layer uses batch normalization and then goes through the Swish function as an activation function. The Swish function prevents the gradient value from being saturated near zero during learning, unlike the Sigmoid and Tanh functions. In addition, unlike Relu, the Swish is less sensitive to the initial value and learning rate. For an enormous negative value, the Swish function returns a value of 0, but it preserves the value to some extent for a small negative value. Squeeze and Excitation Layer is composed of Global Average Pooling-Fully Connected Layer-ReLU-Fully Connected Layer-Sigmoid. The two Fully Connected Layers  
Figure 10: Confusion matrix of SVM-based classifier for Step-2-1.  
Figure 9: Fundus images compressed into \(32\times 32\times 3\) for SVM (RBF kernel).  
prevent the number of parameters from increasing with a bottle-neck structure. Each channel's relative importance can be known through two Fully Connected Layers and nonlinear activation functions (ReLU, Sigmoid). The extracted map can be multiplied by a feature map that skips the Squeeze and Excitation Layer to highlight important features. Finally, the channel is reduced by the \(1\times 1\) convolution operation. For channels reduced to \(1\times 1\), using the activation function deletes sensitive information and it is less likely that sensitive information exists on other channels. Therefore, only batch normalization is used. In this way, the skip-connected input value is concatenated to the output value passed through multiple layers. This concatenation can preserve the previously learned information, learn additionally from it, and reduce memory usage.  
The EfficientNet-B7 model uses fewer parameters and provides the best performance among various EfficientNet models. As shown in Figure 12, the final model we apply for the VA classification in Step-2-2 is completed by transfer learning, in which the convolutional base for feature extraction is reused and the classifier part is directly built by ourselves.  
The parameters we used to train CNN based on EfficientNet-B7 are also given in Table 6.  
In Figure 13a, the classification accuracy of testing data are about 79%, and in Figure 13b, the loss of testing data continues to drop, but the change of the loss value is insignificant after epoch 300. The confusion matrix in Figure 13c is computed with 20% of each class data randomly selected from the original datasets. In the classification report in Figure 13d, the f1-score of Class 3 (Class 0.3-0.7) is 0.51 and that of Class 4 (Class 0.8-1.0) is 0.79. The total f1-score is 0.70. The training of EfficientNet-B7-based CNN seems biased to Class 4, due  
Figure 11: MBConv of EfficientNet [29]. (**a**) MBConv1; (**b**) MBConv6.  
Figure 12: EfficientNet-B7 CNN model for VA classification in Step-2-2.  
to the imbalance of the data quantity, but the overall f1-score of the EfficientNet-B7-based CNN shows that the training of the CNN model is well done.