Eye Lesion Recognition Model Based on ResNet50 and Residual Learning Mechanism

As a vital tool for the diagnosis of ophthalmic diseases, fundus images provide abundant information on retinal structures, playing a crucial role in the early detection and diagnosis of eye conditions. With the widespread application of deep learning technology in the field of medical imaging, automated analysis of fundus images has become one of the key technologies for early detection and classification of ophthalmic diseases. However, due to the complexity and diversity of fundus images, accurate identification of eye diseases plays a significant role in ensuring model performance and generalization capabilities. This study is based on a dataset containing 5770 binocular color fundus images and employs the ResNet50 algorithm for feature extraction and analysis, aiming to accurately identify retinal lesions. By utilizing ResNet50 as the foundational network architecture and incorporating a residual learning mechanism, this research has achieved commendable results in the classification tasks of various eye diseases, including diabetic retinopathy, glaucoma, and cataracts. The experimental results demonstrate that the appropriate algorithm enhances the model's classification accuracy and generalization ability, providing robust technical support for the automated analysis of fundus images.