Research on Myopia Auxiliary Diagnosis System Based on Improved Convolution Neural Network Algorithm

With the rapid development of deep learning technology, its applications in the medical field have become increasingly widespread, particularly in the diagnosis and analysis of ophthalmic diseases. Myopia, as a common refractive error condition, requires accurate diagnosis for the development of effective treatment and management strategies. This study aims to explore the application of deep learning models in the diagnosis of myopia severity in ophthalmology by analyzing biometric data such as fundus images, corneal curvature, and axial length of the eye. We collected a set of ophthalmic examination data from patients with varying degrees of myopia and preprocessed the data, including image denoising, normalization, and feature extraction. Subsequently, we designed and trained a deep convolutional neural network (CNN) model capable of automatically detecting and analyzing features related to myopia from fundus images. The experimental results demonstrate that the proposed deep learning model performs excellently in the diagnosis of myopia severity, showing a high degree of consistency with the diagnostic outcomes of ophthalmic experts. The model not only accurately identifies different levels of myopia but also distinguishes myopia from other refractive conditions. This achievement proves the effectiveness of deep learning in ophthalmic diagnosis, providing new tools for early diagnosis and treatment of myopia. Furthermore, this study also explores the potential advantages of deep learning in ophthalmic diagnosis. Compared to traditional diagnostic methods, deep learning models can process large amounts of data quickly, provide instant feedback, thereby significantly improving diagnostic efficiency. Moreover, as the model's decision-making process is data-driven, it can reduce human errors and enhance the objectivity and consistency of diagnoses.