An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease

Abstract

A cataract is an eye disease that causes visual distortion and the late stage of this disease can lead to blindness. It is considered a silent disease that can occur without the appearance of symptoms. Therefore, the most effective way to detect cataracts is through accurate and timely detection to prevent hurting, expensive operations, and to stop blindness. The purpose of this paper is to propose an automated system based on the wavelet scattering network which categorizes the patients into four classes: early cataract, intermediate cataract, late cataract, and non-cataract conditions using 512 images of the ODIR dataset (212 Cataract and 300 of Normal). The first step in this technique is the preprocessing step for the retinal image was a mean filter, which was utilized to reduce the image's noise. The limited contrast adaptive histogram equalization (CLAHE) method was then employed to improve the image's contract level. Then, Low-variance characteristics can be extracted from image data using a wavelet scattering network for use in deep learning applications. In this network, lowpass scaling filters and predefined wavelets are employed. The average accuracy of the suggested method was 100% for four classes (Normal, Early, Moderate, and Severe). The results are promising compared with other similar works.

Keywords

• Cataract disease, Cataract category, Deep learning network, Retinal fundus images, wavelet scattering.