Deep Learning and Artificial Intelligence-Based System Architecture for the Classification of Diabetic Retinopathy

Authors

  • Ranjith Kumar Siddoju, Dr. Manjunarha Reddy

Keywords:

Microaneurysm, Exudates, Diabetic Retinopathy (DR), Artificial Intelligence, and Medical Imaging.

Abstract

Diabetics are primarily affected by diabetic retinopathy (DR), a dangerous eye condition that can cause blindness. Blood spills across the retina and erratic blood flow are the results of DR in the retina's blood vessels, which damages the macula. The as retinal tissue swells, vision becomes blurry. Microaneurysms (MAs), which are tiny blood vessel dilatation that resemble little red sacs, are the initial sign of diabetic retinopathy.

References

Pratt, Harry, et al. "Convolutional neural networks for diabetic retinopathy." Procedia computer science 90 (2016): 200-205.

Ballamudi, VenkataKoteswara Rao. "Utilization of machine learning in a responsible manner in the healthcare sector." Malaysian Journal of Medical and Biological Research 3.2 (2016): 117-122.

Haloi, Mrinal. "Improved microaneurysm detection using deep neural networks." arXiv preprint arXiv:1505.04424 (2015).

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Published

2018-08-03

How to Cite

Ranjith Kumar Siddoju, Dr. Manjunarha Reddy. (2018). Deep Learning and Artificial Intelligence-Based System Architecture for the Classification of Diabetic Retinopathy. Journal of Computational Analysis and Applications (JoCAAA), 25(8), 86–96. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2129

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