A New and Improved Method for Automated Screening of Age-Related Macular Degeneration Using Ensemble Deep Neural Networks

In this paper, we provide a new framework on deep learning based automated screening method for finding individuals at risk of developing Age-related Macular Degeneration (AMD). We studied the appropriateness of using the transfer learning to screen AMD by using color fundus images. We make use of the Age-Related Eye Disease Study (AREDS) dataset with nearly 150,000 images, which also provided qualitative grading information by expert graders and ophthalmologists. We use ensemble learning technique with two deep neural networks, namely, Inception-ResNet-V2 and Xception with a custom fine-tuning approach. For our study, we have identified two experiments that are most useful in the screening of AMD. First, we have categorized the images into two classes based on the clinical significance: None or early AMD and Intermediate or Advanced AMD. Second, we have categorized the images into four classes: No AMD, early AMD, Intermediate AMD and Advanced AMD. On AREDS dataset, we have achieved an accuracy of over 95.3% for two-class experiment with our ensemble method. With accuracies ranging from 86% (for four-class) to 95.3% (for two-class), we have demonstrated that the training of a deep neural network with the transfer of learned features with a sufficient number of images fares very well and is comparable to human grading.