MacD-Net: An automatic guided-ensemble approach for macular pathology detection using optical coherence tomography images

In this work, a novel guided-ensemble approach has been proposed for an automated diagnosis of three different types of retinal disorders, such as drusen, choroidal neovascularization (CNV) and diabetic macular edema (DME). These conditions, if left untreated, can lead to vision loss. In the proposed approach, four different pre-trained CNNs such as MobileNetV1, EfficientNetB3, NASNetMobile and XceptionNet have been fine-tuned for that purpose and the prediction score obtained by each CNN in an ensemble has been combined based on their validation accuracies. The proposed guided-ensemble approach has been trained and tested on the public dataset consisting of 84 495 retinal OCT images for training and 1000 unseen images for testing. These images were graded by the experts into four categories, such as CNV, DME, drusen and normal. The proposed approach has outperformed the other methods by obtaining an accuracy of 99.8% in classifying four different classes of retinal OCT images.