Automated Macular Disease Detection using Retinal Optical Coherence Tomography images by Fusion of Deep Learning Networks

This work proposes a method to improve the automated classification and detection of macular diseases using retinal Optical Coherence Tomography (OCT) images by utilizing the fusion of two pre trained deep learning networks. The concatenation of feature vectors extracted from each of the pre trained deep learning model is performed to obtain a long feature vector of the fused network. The experimental results proved that the fusion of two Deep Convolution Neural Network (DCNN) achieves better classification accuracy compared to the individual DCNN models on the same dataset. The automated retinal OCT image classification can assist the large-scale screening and the diagnosis recommendation for an ophthalmologist.