International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 9, Issue 12 (December 2022), Pages: 135-144

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 Original Research Paper

 Comparative study on early recognition and identifying diabetic retinopathy with different layers in CNN

 Author(s): Gorli L. Aruna Kumari 1, *, Poosapati Padmaja 2, Jaya G. Suma 3

 Affiliation(s):

 1Department of CSE, Gitam School of Technology, Gitam Deemed to be University, Visakhapatnam, India
 2Department of IT, Anil Neerukonda Institute of Technology and Science, Visakhapatnam, India
 3Department of IT, College of Engineering, Jawaharlal Nehru Technological University, Kakinada, India

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-8856-5465

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.12.017

 Abstract:

Diabetes is the most prevalent condition worldwide, and diabetic retinopathy (DR) is a subsequent condition caused by acute diabetic cases. It causes severe degeneration of the retina. The compounding blood vessels bloat and often burst, causing fluid leaks in the aqueous humor. This, in turn, causes the creation of undesirable nerve fiber infractions from the occlusion of arteries. Diagnosis requires a manual retinal examination that can often be inconsistent and deliberate with potential flaws in the diagnosis. Early detection through an ophthalmologist is paramount to prevent the prognosis of severe vision loss. Considering the current leap of machine learning in the field of healthcare, early detection of DR can be potentially made efficient with intelligent systems. This research proposes methodologies to fine-tune the existing pre-trained architectures, attaining the classification accuracies of 98% to classify the ocular fundus images which identify early prediction of diabetes. Additionally, this study presents an exposition of other equally scrutinized approaches to ultimately showcase a deep neural network architecture that can precisely classify normal fundus and degenerated fundus from the lowest to the most severe hierarchy. Among several layers in the CNN model pre-tuning and post-tuning exception layers outperformed with good results.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Deep neural network, Classification, Convolution neural network, Data mining, Diabetic retinopathy

 Article History: Received 10 March 2022, Received in revised form 9 June 2022, Accepted 7 September 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Kumari GLA, Padmaja P, and Suma JG (2022). Comparative study on early recognition and identifying diabetic retinopathy with different layers in CNN. International Journal of Advanced and Applied Sciences, 9(12): 135-144

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 Figures

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 Tables

 Table 1 

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