Efficient Fundus Image Gradeability Approach Based on Deep Reconstruction-Classification Network

Abstract

Quality of retinal image is vital for screening of ailments pertaining to eye such as glaucoma, diabetic retinopathy (DR) and age related macular degeneration. Therefore, assessing quality of retinal image prior to any kind of diagnosis has assumed significance in Computer Aided Desgin (CAD) applications. The rationale behind this is that reliability of retinal image is to be guaranteed to have dependable diagnosis. In this paper, we propose a novel retinal fundus image quality assessment (RIQA) method based on autoencoder network to assess retinal images if the image is acceptable for screening or not. The autoencoder network architecture is well suited to precisely to properly represent the key features of the image quality, especially when the network can correctly reconstruct the input image. The proposed model consists of encoder and decoder successive networks. The encoder will be used for representing the features of the input image. In turn , the decoder will be used for reconstruct the input image. The features get from encoder network will then be fed to a classifier in order to classify the quality of retinal image to two classes: gradable or ungradable. The experimental results revealed more useful assessment and the proposed deep model provides a superior performance for RIQA. Thus, our model can serve real-world Clinical Decision Support Systems in the healthcare domain.