VinceptionC3D: a 3D convolutional neural network for retinal OCT image classification

Abstract

In order to make further and more accurate automatic analysis and processing of optical coherence tomography (OCT) images, such as layer segmentation, disease region segmentation, registration, etc, it is necessary to screen OCT images first. In this paper, we propose an efficient multi-class 3D retinal OCT image classification network named as VinceptionC3D. VinceptionC3D is a 3D convolutional neural network which is improved from basic C3D by adding improved 3D inception modules. Our main contributions are: (1) Demonstrate that a fine-tuned C3D which is pretrained on nature action video datasets can be applied for the classification of 3D retinal OCT images; (2) Improve the network by employing 3D inception module which can capture multi-scale features. The proposed method is trained and tested on 873 3D OCT images with 6 classes. The average accuracy of the C3D with random initialization weights, the C3D with pre-trained weights, and the proposed VinceptionC3D with pre-trained weights are 89.35%, 92.09% and 94.04%, respectively. The result shows that the proposed VinceptionC3D is effective for the 6-class 3D retinal OCT image classification.