Wavelet scattering transform application in classification of retinal abnormalities using OCT images.

ABSTRACT: To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. 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. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a

SUBMITTER: Baharlouei Z 

PROVIDER: S-EPMC10624695 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature