OCTx: Ensembled Deep Learning Model to Detect Retinal Disorders

In this paper, we deconstruct and demonstrate a detection framework to classify Retinal Optical Coherence Tomography (OCT) images across three classes namely, Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), and the DRUSEN from normal Retina. In this research, we developed on a Deep Ensemble Network by the virtue of which we were able to obtain a state-of-the-art accuracy of 98.53% on our test image dataset that was deliberately increased to 12% of the total images. Further, we also took advantage and insight from a feature map obtained from our convolutional layers to build our final model, which we call Optical Coherence Tomography Extended (OCTx). In our experiments, we found that OCTx was more accurate and diverse as compared to previously reported works that were validated on the exact same dataset.