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Aggregated residual transformation network for multistage classification in diabetic retinopathy
Author(s) -
Sambyal Nitigya,
Saini Poonam,
Syal Rupali,
Gupta Varun
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22513
Subject(s) - diabetic retinopathy , overfitting , residual , abnormality , computer science , blindness , artificial intelligence , sensitivity (control systems) , transformation (genetics) , retinopathy , stage (stratigraphy) , pattern recognition (psychology) , medicine , algorithm , diabetes mellitus , artificial neural network , optometry , paleontology , biochemistry , chemistry , psychiatry , electronic engineering , biology , engineering , gene , endocrinology
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.