Iterative fusion convolutional neural networks for classification of optical coherence tomography images

Optical coherence tomography (OCT) can achieve the high-resolution 3D tomography imaging of the retina, which is crucial for the diagnosis of retinal diseases. Currently, the classification of retinal OCT images is mainly conducted by ophthalmologists, which is time consuming and subjective. In this paper, we propose an iterative fusion convolutional neural network (IFCNN) method for the automatic classification of retinal OCT image. In the convolutional neural network (CNN), different convolutional layers contain feature information from different scales. Therefore, the proposed network adopts an iterative fusion strategy, which iteratively combines features in current convolutional layer with those of all previous layers in the CNN network, and thus can jointly utilize the features of different convolutional layers to achieve accurate classification of OCT images. Experimental results on a real retinal OCT dataset and a musculoskeletal radiographs dataset demonstrate the superiority of the proposed method over the traditional CNN and several well-known OCT classification methods.