Automatic Cataract Severity Detection and Grading Using Deep Learning

Abstract

Cataracts are an eye condition that causes the eye’s lens to become cloudy and is a significant cause of vision loss worldwide. Accurate and timely detection and diagnosis of cataracts can prevent vision loss. However, poor medical care and expensive treatments prevented cataract patients from receiving appropriate treatment on time. Therefore, an inexpensive system that diagnoses cataracts at an early stage needs to be developed. This study proposes an automatic method for detecting and classifying cataracts in their earliest stages by combining a deep learning (DL) model with the 2D-discrete Fourier transform (DFT) spectrum of fundus images. The proposed method calculates the spectrogram of fundus images using a 2D-DFT and uses this calculated spectrogram as an input to the DL model for feature extraction. After feature extraction, the classification task is performed by a softmax classifier. This study collected fundus images from various open-source databases that are freely available on the Internet and classified them into four classes based on an ophthalmologist’s assessment. All the collected fundus images from various datasets with open access are unsuitable for cataract diagnosis. Consequently, a module for identifying the fundus images of good and poor quality is also incorporated into this method. The experimental results show that the proposed system can outperform previous state-of-the-art works by a significant margin compared to a benchmark of four-class accuracy and achieves the four-class accuracy of 93.10%.