Deep Learning Feature Fusion-Based Retina Image Classification

Aiming at the problems of missed detection and low efficiency in manual classification and diagnosis of optical coherence tomography retina images, a deep learning-based convolutional network classification algorithm is proposed to construct joint multilayer features. First, retinal images are preprocessed using the mean shift and data normalization algorithm. The loss function weighting algorithm is combined to solve the data imbalance problem. Second, a lightweight deep separable convolution rather than an ordinary convolution layer is used to reduce the number of model parameters. Global average pooling replaces fully connected layers to increase spatial robustness, and different convolutional layers are used to build feature fusion layers to enhance feature circulation between layers. Finally, the SoftMax classifier is used for image classification. Experimental results show that the model can achieve 97 A, 95 A, and 97% in accuracy, precision, and recall, respectively, thereby reducing the recognition time. The proposed deep learning feature fusion-based method performs well in the classification and diagnosis of retinal images.