# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 2 Related Work
### Cataract Fundus Image Recognition  
In medical diagnosis, the diagnosis of cataract usually relies on slit lamp microscopy to observe the degree of opacity of the lens, combined with comprehensive information such as the patient's visual condition and medical history. However, due to the lack of resources of professional ophthalmologists and the large number of patients, many patients are not able to get treatment in a timely manner. In 2010, a study ([PERSON] et al., 2018) first proposed a method of using AI to recognize slit lamp microscope images to diagnose nuclear cataracts, and established a 38-point shape model to identify the nuclear region of the lens and extract key features from it for grading. Grading is based on a comparison of the patient image to four standard images. The results show that only under the premise of accurate structure recognition can the features be extracted to achieve automatic grading. Unlike previous traditional methods of averaging the entire lens, the system is able to automatically identify nuclear regions in slit lamp images, and test results in more than 5000 images show that about 0.95 of the images can be automatically diagnosed. However, this technology still faces certain limitations, especially when image quality is limited or the nuclear region is occluded. In addition, the system is designed with user intervention to deal with special cases caused by inaccurate focal length, small pupils or ptosis, etc., although the system adaptability is enhanced, but the manual intervention also reflects the strong dependence of the algorithm on specific samples, which limits its ability to fully automate.  
With the advancement of artificial intelligence, cataract diagnosis algorithms have been further improved and optimized. For example, in 2019, researchers proposed a novel \"multi-feature fusion\" strategy based on deep learning ([PERSON] et al., 2016; [PERSON] et al., 2018), which can classify cataracts into six different grades based on fundus images. Firstly, the deep neural network is used to extract fundus image features, and texture features are extracted from the original image and vascular image, and the classification method of multi-model training and ensemble learning is used to reduce the classification error and improve the accuracy of hierarchical diagnosis. The final results showed that the accuracy of the method for the six-level classification of cataracts reached an average of 0.9266, and the accuracy of the four-level classification was as high as 0.9475, which was at least 0.0175 higher than that of the traditional method. However, this method is effective when the image dataset is rich and the training is sufficient, but it has high requirements for data distribution and quality, and the effect may be reduced for poor images or noisy samples.  
Although these new methods provide significant improvements in diagnostic efficiency, it is still necessary to consider their generalization ability and stability under different populations and fundus structural differences. The multi-feature fusion strategy performs well in the diagnosis of moderate-to-severe cataracts, but the detection of mild cataracts is uncertain, and there are adaptability problems in the environment, with limited shooting conditions. In addition, over-reliance on AI systems may lead doctors to overlook some subtle pathological features [16]. In the future, such algorithms should be further validated in multi-ethnic and multi-environment databases, and the reliability of clinical application should be improved based on doctors' experience to ensure that they are both efficient and robust in practical applications.