  Convolutional Neural Network models have successfully detected retinal
illness from optical coherence tomography (OCT) and fundus images. These CNN
models frequently rely on vast amounts of labeled data for training, difficult
to obtain, especially for rare diseases. Furthermore, a deep learning system
trained on a data set with only one or a few diseases cannot detect other
diseases, limiting the system's practical use in disease identification. We
have introduced an unsupervised approach for detecting anomalies in retinal
images to overcome this issue. We have proposed a simple, memory efficient,
easy to train method which followed a multi-step training technique that
incorporated autoencoder training and Multi-Scale Deep Feature Sparse Coding
(MDFSC), an extended version of normal sparse coding, to accommodate diverse
types of retinal datasets. We achieve relative AUC score improvement of 7.8\%,
6.7\% and 12.1\% over state-of-the-art SPADE on Eye-Q, IDRiD and OCTID datasets
respectively.
