# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 3 Method
### Data Preprocessing  
Identification of ocular diseases based on fundus image datasets is an important branch in medically assisted diagnosis, especially in the diagnosis and research of ophthalmic diseases such as cataracts. However, due to the quality and scale of fundus image datasets, there are many challenges in auxiliary diagnosis. When it comes to data quality, images can have artifacts, or be out of focus, underexposed, or overexposed due to shooting conditions, equipment quality, or patient cooperation. At the same time, due to the need for professional knowledge for image annotation, the same image may appear to be labeled with different annotations. In addition, some common eye diseases (e.g., myopia) may have far more image samples than other diseases, resulting in an imbalance of categories in the dataset, which may affect the model's ability to identify a small number of categories. Data size issues: High-quality fundus image datasets may be limited by the difficulty and cost of data collection, resulting in a limited number of samples being available; datasets need to be representative of real-world diversity, including samples of different ages, genders, ethnicities, and stages of disease. If the dataset lacks diversity, this may affect the generalization ability of the model; as medical knowledge advances and diagnostic criteria are updated, existing datasets may need to be updated and maintained regularly to maintain their relevance and validity. In order to solve the above problems, this paper collects three datasets and extracts cataract-related fundus images to form the final dataset. Table 1 shows the distribution of the datasets. In order to solve the above problems, this paper combines three datasets, and then extracts cataract-related fundus images and adopts data augmentation operations. Data augmentation is an important technique to improve the generalization ability and performance of models. The dataset is enriched by a series of transformations of the original image to generate a new image sample. In the field of medical imaging, the application of data augmentation is particularly important due to the high cost of acquiring a large number of annotated images. There are a variety of strategies that can be employed when performing data augmentation of cataract fundus images. Common data augmentation operations include rotating, scaling, clipping, flipping, and adding noise, which can increase the diversity of data while better simulating real-world clinical scenarios and maintaining the same image content. For example, rotation can simulate different angles of fundus images caused by the natural tilt of the patient's head, and zoom can simulate imaging at different focal lengths. When applying data augmentation, this paper adopts a series of image transformation operations to enrich the diversity of data samples and improve the generalization ability of the model. These data enhancement operations include rotation, scaling, cropping, flipping, and brightness and contrast adjustments, etc., and in order to avoid introducing excessive noise or distortion, the amplitude scale of each enhancement operation is set to 0.2. Specifically, the angle range of the rotation operation is set to \(\pm\)20\({}^{\circ}\), the zoom is limited to 0.2, and the brightness and contrast adjustments do not vary by more than 0.2. These settings enable the enhanced image to retain the key features of the original data, avoiding the distortion of image features caused by excessive transformation, so that the model can effectively learn the key features of the disease. In addition, by controlling the amplitude of the enhancement, it is ensured that the data transformation is carried out within a reasonable range, so as to avoid the model learning irrelevant features or noise [17]. Excessive data augmentation may make it difficult for the model to identify the true features of the lesion area, and even cause the model to overfit the noise.