Aggregated residual transformation network for multistage classification in diabetic retinopathy. Issue 2 (23rd October 2020)

• Record Type: Journal Article
• Authors: Sambyal, Nitigya; Saini, Poonam; Syal, Rupali; Gupta, Varun
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 2(2021)
• Page Start: 741
• Page End: 752
• Publication Date: 2020-10-23
• Subjects: aggregated residual transformations -- computer‐aided diagnosis -- convolutional neural networks -- diabetic retinopathy -- multistage classification -- ResNeXt; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22513
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Diabetic Retinopathy is a retinal abnormality which is characterized by progressive damage to the retina, eventually leading to irreversible blindness. In this paper, we propose an aggregated residual transformation‐based model for automatic multistage classification of diabetic retinopathy. The proposed model obtains 99.68% overall classification accuracy, 99.68% sensitivity, 99.89% specificity and 99.68% precision without overfitting on the MESSIDOR dataset. Further, the model obtains an accuracy of 99.89% for stage 0, 99.89% for stage 1, 99.68% for stage 2 and 99.89% for stage 3 of diabetic retinopathy. In comparison to residual network, the model shows an overall accuracy gain of 0.52%. The model also ensures an overall improvement of more than 6% in accuracy, 1.2% in sensitivity and 2.43 % in specificity when compared to best results reported in the literature. The proposed work outperforms the existing methods and achieves state‐of‐the‐art results for the multistage classification of diabetic retinopathy.