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The present study proposes a new approach to automated screening ofClinically Significant Macular Edema (CSME) and addresses two major challengesassociated with such screenings, i.e., exudate segmentation and imbalanceddatasets. The proposed approach replaces the conventional exudate segmentationbased feature extraction by combining a pre-trained deep neural network withmeta-heuristic feature selection. A feature space over-sampling technique isbeing used to overcome the effects of skewed datasets and the screening isaccomplished by a k-NN based classifier. The role of each data-processing step(e.g., class balancing, feature selection) and the effects of limiting theregion-of-interest to fovea on the classification performance are criticallyanalyzed. Finally, the selection and implication of operating point on ReceiverOperating Characteristic curve are discussed. The results of this studyconvincingly demonstrate that by following these fundamental practices ofmachine learning, a basic k-NN based classifier could effectively accomplishthe CSME screening.

An Efficient Framework for Automated Screening of Clinically Significant Macular Edema