Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features

TL;DR

This paper presents a novel multi-class multiple-instance learning approach using color correlogram features for automatic classification of diabetic retinopathy images, effectively distinguishing between normal, microaneurysm, and neovascularization cases.

Contribution

It introduces a spectrally tuned, low-dimensional color auto-correlogram feature combined with a multiple-instance learning framework for improved DR image classification.

Findings

Outperforms state-of-the-art methods significantly

Effective in distinguishing small localized lesions

Demonstrates high accuracy in 3-class classification

Abstract

All people with diabetes have the risk of developing diabetic retinopathy (DR), a vision-threatening complication. Early detection and timely treatment can reduce the occurrence of blindness due to DR. Computer-aided diagnosis has the potential benefit of improving the accuracy and speed in DR detection. This study is concerned with automatic classification of images with microaneurysm (MA) and neovascularization (NV), two important DR clinical findings. Together with normal images, this presents a 3-class classification problem. We propose a modified color auto-correlogram feature (AutoCC) with low dimensionality that is spectrally tuned towards DR images. Recognizing the fact that the images with or without MA or NV are generally different only in small, localized regions, we propose to employ a multi-class, multiple-instance learning framework for performing the classification task using the proposed feature. Extensive experiments including comparison with a few state-of-art image classification approaches have been performed and the results suggest that the proposed approach is promising as it outperforms other methods by a large margin.