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  Published Paper Details:

  Paper Title

A DEEP LEARNING APPROACH FOR DIABETIC RETINOPATHY DETECTION

  Authors

  Supriya.A,  Dr. Rajeshwari. J

  Keywords

Convolution Neural Network, Deep learning, diabetic retinopathy, Kaggle dataset, Dropout.

  Abstract


Diabetic retinopathy is an eye ailment caused due to prolonged diabetes. If unacknowledged or ineffective treatment can lead to irreversible vision loss. This ailment can be identified by scrutinizing colour fundus photographs of the human retina. Our work assesses in deep learning field which is used for the detection and class segregation of diabetic retinopathy into binary or multiclass and collate them based on different metric measures like precision, recall, accuracy. The classification of diabetic retinopathy involves multiple steps like pre- processing of image, feature extraction, dimensionality reduction and classification of exudates, woolen spot, hemorrhages & microaneurysm. In this paper, Deep neural network approach is implemented by making use of online available Kaggle dataset, various metrics are calculated and applied pre-processing techniques.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2009141

  Paper ID - 198583

  Page Number(s) - 1036-1041

  Pubished in - Volume 8 | Issue 9 | September 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Supriya.A,  Dr. Rajeshwari. J,   "A DEEP LEARNING APPROACH FOR DIABETIC RETINOPATHY DETECTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 9, pp.1036-1041, September 2020, Available at :http://www.ijcrt.org/papers/IJCRT2009141.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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