TCAM-Resnet: A convolutional neural network for screening DR and AMD based on OCT images

Abstract

Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are important causes of blindness and visual loss. Optical coherence tomography (OCT) is a non-invasive optical imaging method that can capture retinal vascular information and even pathological information. In order to improve the screening rate and accuracy of these two diseases, we propose a new network structure named TCAM-Resnet, which uses OCT three-dimensional images to screen and classify AMD and DR. TCAM-Resnet is based on the Resnet network. A three-dimensional convolution attention module (TCAM) is added. The attention module can extract the weight features of blood vessels from 3D images and uses the residual-like structure when interacting with the Resnet network, which makes the original data retain more information during attention. Experimental results on the OCTA-500 dataset show that a three-dimensional convolution network is superior to a two-dimensional convolution network in lesion feature extraction. With the addition of the new module TCAM, Resnet3D has achieved higher accuracy in disease classification t asks, with the accuracy of AMD, DR, and NORMAL reaching 83.3%, and the accuracy of AMD and NORMAL reaching 98%.