Deep CNN framework for retinal disease diagnosis using optical coherence tomography images

Accurate and robust diagnosis of retinal diseases using OCT imaging is considered an essential part for clinical utility. We propose a deep learning based, a fully automated diagnosis system for detecting retinal disorders namely, Drusen macular degeneration (DMD) and diabetic macular edema (DME) using optical coherence tomography (OCT) Images. If it is not diagnosed and treated, these degenerative abnormalities may result in moderate to severe vision loss. Early detection of these diseases reduces the risk of further complications and expedites the treatment process. We propose a deep convolutional neural network (CNN) framework for the diagnosis and classification into Normal, DMD and DME effectively. First, despeckling of the input OCT images is performed using the Kuan filter to remove inherent speckle noise. Further, the CNN network is tuned using hyperparameter optimization techniques. Additionally, K-fold validation is performed to ensure complete usage of the dataset. We evaluate the proposed model with number of performance metrics using Mendeley database consisting of labelled OCT images. The resulting classification accuracy of the proposed model is 95.7%. Further, an authoritative study is performed between the pre-trained models and proposed framework using the acquired performance metrics to demonstrate the efficacy of our model.