Next, a Principal Component Analysis classifies the extracted features. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations.
We use two layers of the WST network to obtain a direct and efficient model. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role.
