Detection of Retinal Abnormalities in OCT Images Using Wavelet Scattering Network

Diagnosis retinal abnormalities in Optical Coherence Tomography (OCT) images assist ophthalmologist in the early detection and treatment of patients. To do this, different Computer Aided Diagnosis (CAD) methods based on machine learning and deep learning algorithms have been proposed. In this paper, wavelet scattering network is used to identify normal retina and four pathologies namely, Central Serous Retinopathy (CSR), Macular Hole (MH), Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). Wavelet scattering network is a particular convolutional network which is formed from cascading wavelet transform with nonlinear modulus and averaging operators. This transform generates sparse, translation invariant and deformation stable representations of signals. Filters in the layers of this network are predefined wavelets and not need to be learned which causes decreasing the processing time and complexity. The extracted features are fed to a Principal Component Analysis (PCA) classifier. The results of this research show the accuracy of 97.4% and 100% in diagnosis abnormal retina and DR from normal ones, respectively. We also achieved the accuracy of 84.2% in classifying OCT images to five classes of normal, CSR, MH, AMD and DR which outperforms other state of the art methods with high computational complexity. Clinical Relevance- Clinically, the manually checking of each OCT B-scan by ophthalmologists is tedious and time consuming and may lead to an erroneous decision specially for multiclass problems. In this study, a low complexity CAD system for retinal OCT image classification based on wavelet scattering network is introduced which can be learned by a small number of data.