# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 5 Ablation Experiments  
In this paper, ablation experiments were performed to evaluate the contribution of different components in the model to the overall performance. By progressively removing or modifying parts of the model, we can observe the impact of these changes on the performance of the model and understand the role and importance of each component. The ablation experiments in this paper mainly focus on the selection of backbone networks and the impact of the addition of the SE_block and prototype network classifiers on the performance of the model. First, we performed ablation experiments on different backbone networks (see Table 2), including CNN, VGG16, ResNet50, ResNet152, and InceptionV3. These networks were trained and tested on the same dataset, and the results are shown in Table 2. As can be seen from the table, ResNet50 is better than other backbone networks in terms of accuracy, AUC (area under the curve), and F1 score, which indicates that ResNet50 has a strong ability  
Figure 8: Confusion matrix for the cataract detection model. Figure 7: Model training curves. in feature extraction and can learn features that are helpful for classification from fundus images more effectively. In the ablation experiment, we not only looked at the impact of the backbone network model selection model, but also looked at the performance when two modules were added to other backbone networks. This comparative analysis helps us to understand more comprehensively the role and effect of the SE_block and prototype network classifiers in different model frameworks. Next, we performed ablation experiments on the ResNet50 model, the ResNet152 model, and the InceptionV3 model to evaluate the impact of the SE_block and prototype network classifiers. The experimental results are shown in Table 3. The original ResNet50 model already performed well in terms of accuracy, AUC, and F1 score, but the performance of the model was further improved with the addition of the SE_block and prototype network classifiers. Specifically, the accuracy of the ResNet50 model increased from 0.9622 to 0.9875, the AUC increased from 0.9878 to 0.9984, and the F1 score increased from 0.9797 to 0.9855. At the same time, according to the obtained results, it can be seen that the performance of the model also significantly improved after adding the SE_block and prototype network classifiers to the two backbone networks: ResNet152 and InceptionV3. For the ResNet152 model, the original model had an accuracy of 0.9567, an AUC of 0.9723, and an F1 score of 0.9651. After adding the SE_block and prototype network classifiers, the accuracy rate increased by 1.16 percentage points and the F1 score increased by 0.74 percentage points. For the InceptionV3 model, the accuracy of the original model was 0.8694, the AUC was 0.8412, and the F1 score was 0.8765. After adding the SE_block and prototype network classifiers, the accuracy increased by 2.61 percentage points, and the AUC increased by 3.1 percentage points. This indicates that the introduction of the SE_block and prototype network classifiers not only enhances the expressive ability of features, but also improves the classification accuracy of cataract fundus images in the model. From these ablation experiments, we can draw the following conclusions:  
1. By introducing the channel attention mechanism, the SE_block can enhance the expression ability of features in different models and improve the sensitivity of the model to key information. 2. As a small-shot learning method, the prototype network classifier can improve the generalization ability of the model to new samples, especially in the field of medical images, which helps to improve the accuracy of classification and the explanatory nature of the model. 3. Different backbone networks can achieve performance improvement after fusing the SE_block and prototype network classifiers, but the magnitude of the improvement may vary depending on the model.