# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 1 Introduction
### Research Issues  
Reduced light transmission or color change in the lens is called a cataract, which is a degenerative eye disease [3]. This lesion can weaken vision and in severe cases lead to complete blindness. It is associated with a number of factors, including aging, genetic factors, external injuries, and metabolic disorders. The prevalence of cataracts continues to rise as the global population ages. According to the World Health Organization [4], the number of people blinded by cataracts may increase to 40 million by 2025. Cataracts are the leading cause of vision loss globally and are responsible for half of all blindness in low- and middle-income countries [5; 6]. Cataract fundus vs. normal fundus images are shown in Figure 1.  
Currently, there are many issues as well as challenges regarding deep learning models for assisted diagnosis of cataract disease:  
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2. Scarcity and imbalance of datasets: Obtaining large datasets of cataract fundus images may be difficult because cataract diagnosis usually requires specialized medical equipment and doctors with specialized skills. In addition, the collection and use of such data requires extra care and handling due to privacy and ethical considerations [7]. Meanwhile, the number of samples of healthy fundus images in a given cataract image dataset is much larger than the number of samples of diseased images. This imbalance may result in deep learning models having better recognition ability for a larger number of categories and insufficient recognition ability for a smaller number of categories.
3. Model generalization ability: Existing diagnostic methods rely heavily on the subjective judgment of professional ophthalmologists, which is a limiting factor in resource-poor areas, and the characteristics of cataract fundus images may vary across regions  
Figure 1: Cataract fundus image (**left**) vs. normal fundus (**right**).  
and populations. How to design deep learning models to ensure that they have good generalization ability and can adapt to image features in different devices and populations is an extremely critical issue.
3. Interpretability of models: Deep learning models are often considered as \"black box\" models, and their decision-making process lacks transparency. Through effective extraction and enhancement of features, and combined with actual clinical practice, the interpretability of deep learning models can be improved to truly achieve the purpose of assisted diagnosis, so that doctors and patients can understand the diagnostic basis of the model. The main contributions of this paper can be summarized as follows: 1. In this paper, we propose a new hybrid deep learning network for cataract fundus image-assisted diagnosis, which aims to enhance the importance of cataract fundus image channel features to improve the generalization ability and classification accuracy of the model. 2. By integrating multiple open-source cataract fundus image classification datasets and applying data augmentation technology, a more diverse and representative fundus image dataset was constructed to solve the problems of unbalanced image labeling and quantity distribution in a single dataset. 3. Through ablation experiments, the effectiveness of the backbone network and each component used in the model was verified, especially the key role of the SE_block and prototype networks in improving the performance of the model, and excellent performance was achieved in the cataract fundus image recognition task. 4. In terms of model interpretation, the model enhances the sensitivity of the network to key information features, highlights the feature channels that have the greatest impact on the classification decision, and finally the distance-based classification method also provides an intuitive explanation for the model's decision making.