Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features

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.

Automatic detection of referral patients due to retinal pathologies through data mining

“…Extensions to the identification of multiple local patterns have been presented (Foulds and Frank, 2010;Quellec et al, 2012). Recently, MIL has been applied to eye fundus examinations (Yu et al, 2004;Quellec et al, 2012;Venkatesan et al, 2012). Besides MIL, a few anomaly detectors have been presented for specific regions of the retina: the optic disc (Kavitha and Ramakrishnan, 2010;Zhu et al, 2014) and the retinal vasculature (Kavitha and Ramakrishnan, 2010).…”

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

“…Most automated systems [3][5] [10] use hand-crafted image features, such as shape, color, brightness and domain knowledge of diabetic retinopathy, which includes optic disk, blood vessels and macula. Since 2012, Convolution Neural Networks (CNN) have shown remarkable performance in image classification tasks [7].…”

A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images

“…Techniques to improve contrast and sharpness and reduce noise are therefore required as an aid for human interpretation of the fundus images are called image enhancement as a first step towards automatic analysis of the fundus images. Standard contrast stretch techniques a functional by (Sinthanayothin et al, 1999;Osareh et al, 2001;Goldbaum et al, 1990); methods allowed to enrich certain features (e.g., only micro aneurysms), are applied in restoration techniques for images with very poor feature (e.g., due to cataracts) have been applied in (Cree et al, 1999;Venkatesan et al, 2012) The annual retinal examination and early detection of DR can considerably reduce the risk of visual loss in diabetic individuals (Rema and Pradeepa, 2007).…”