# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 2 Related Work  
### Application of Deep Learning in Medical Image Recognition  
In recent years, ophthalmic diseases have become the leading cause of vision loss, and the development of deep learning technology has brought new opportunities for early diagnosis and treatment. Deep learning models such as convolutional neural networks (CNNs) perform well in image recognition tasks, so they are widely used in ophthalmic medical image analysis.  
Google's medical imaging research group has developed a deep learning-based algorithm that can automatically detect lesions such as microhemangiomas, hemorrhages, and exudates in retinal images. The algorithm first uses transfer learning to pre-train the model foundation on a massive general image dataset, and then fine-tunes it to adapt to a smaller medical image dataset. In terms of detecting DR, its performance is comparable to that of professional ophthalmologists, with a TP value of 0.95 and a TN value of 0.94 [8]. While these results are encouraging, the generalization ability of this algorithm in different populations and pathological changes must be carefully considered, especially when the data volume is small and diversity is limited. The deep learning model developed by [PERSON] et al. achieves high accuracy in the detection and classification of AMD at different stages, with an AUC value of 0.89 through fundus image analysis, combined with local details and global contextual information, and multi-scale feature and depth feature fusion technology [9]. Still, this technology falls short when it comes to the recognition of subtle changes caused by AMD at different stages; especially in the early and late stages of disease progression, the accuracy of the model may be limited by data noise or image quality, which can affect the decision support for disease prognosis and treatment options. The deep learning model developed by [PERSON] et al. utilizes an attention mechanism that enables the model to focus on areas of the image that are closely related to glaucoma development, with a detection accuracy of 0.90, a TP value of 0.87, and a TN value of 0.92 [10]. However, the complex pathological characteristics of glaucoma and individual differences may affect the accuracy of the model, especially in the early stages of the lesion, where the changes in the optic nerve head are subtle and may not be captured by the algorithm. Therefore, while these deep learning models demonstrate impressive diagnostic capabilities, their generalization capabilities and stability need to be treated with caution in practical applications. Future studies can validate models with large-scale, multicenter, multi-ethnic medical image datasets to ensure their reliability and interpretability in clinical applications.  
Deep learning techniques are promising for application to ophthalmic disease recognition and have demonstrated their effectiveness in several disease areas. [PERSON] et al. (2018) integrated the SE module into CNNs such as ResNet and DenseNet to enhance the focus on tumor regional features in the study of breast cancer diagnosis, thereby improving the model's classification performance for benign and malignant tumors. However, the channel attention mechanism of the SE module focuses on the weight allocation of global features, and may ignore some subtle features in small or marginal regions of tumor images. [PERSON] et al. (2018) proposed SE-UNet in their study, applying the SE module to the encoding and decoding module of U-Net to improve the segmentation effect of skin lesions, but the effect may be reduced on low-resolution or noisy images. [PERSON] et al. (2018) used the SE module to improve the ResNet structure for distinguishing X-ray images from normal images in COVID-19 patients, as well as images from other lung diseases, and achieved better diagnostic results than traditional CNNs. However, the model focuses on the feature distribution of the training dataset, which leads to the poor generalization ability of the model under new data or changes in imaging conditions.