LightOCT: Exploring the depth for Retinal disease detection

With the advancement in technology and computation capabilities, identifying retinal damage through state-of-the-art CNNs architectures has led to the speedy and precise diagnosis, thus inhibiting further disease development. In this study, we focus on the classification of retinal damage caused by diabetic macular edema (DME), Choroidal Neovascularization (CNV),DRUSEN, and NORMAL in optical coherence tomography (OCT) images. The emphasis of our experiment is to investigate the component of depth in the neural network architecture. We introduce a shallow convolution neural network - LightOCT, outperforming the other deep model configurations, with the lowest value of LVCEL and highest accuracy (+98% in each class). Next, we experimented to find the best fit optimizer for LightOCT. The results proved that the combination of LightOCT and Adam gave the most optimal results. Finally, we compare our approach with transfer learning models, and LightOCT outperforms the state-of-the-art models in terms of computational cost, least training time and gives comparable results in the criteria of accuracy. We would like to focus our future efforts on improving the accuracy metrics using shallow models, such that the trade-off between training time and accuracy is reduced..