Diabetic Retinopathy Diagnosis from Fundus Images Using Stacked Generalization of Deep Models

Abstract

Diabetic retinopathy (DR) is a diabetes complication that affects the eye and can cause damage from mild vision problems to complete blindness. It has been observed that the eye fundus images show various kinds of color aberrations and irrelevant illuminations, which degrade the diagnostic analysis and may hinder the results. In this research, we present a methodology to eliminate these unnecessary reflectance properties of the images using a novel image processing schema and a stacked deep learning technique for the diagnosis. For the luminosity normalization of the image, the gray world color constancy algorithm is implemented which does image desaturation and improves the overall image quality. The effectiveness of the proposed image enhancement technique is evaluated based on the peak signal to noise ratio (PSNR) and mean squared error (MSE) of the normalized image. To develop a deep learning based computer-aided diagnostic system, we present a novel methodology of stacked generalization of convolution neural networks (CNN). Three custom CNN model weights are fed on the top of a single meta-learner classifier, which combines the most optimum weights of the three sub-neural networks to obtain superior metrics of evaluation and robust prediction results. The proposed stacked model reports an overall test accuracy of 97.92% (binary classification) and 87.45% (multi-class classification). Extensive experimental results in terms of accuracy, F-measure, sensitivity, specificity, recall and precision reveal that the proposed methodology of illumination normalization greatly facilitated the deep learning model and yields better results than various state-of-art techniques.