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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM41734

Retinal Diseases Classification using Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM41734

Volume:

9 |

2 |

Feb 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM41734

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PSS

Prof Sayali Shivarkar1

UB

Unmesh Bhunia2

SA

Shrihari Ambekar3

SW

Samarth Walunj4

SS

Soham Songire5

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Abstract

Retinal diseases, encompassing a wide spectrum of ocular disorders, pose significant threats to vision and overall eye health. Early and accurate diagnosis is crucial for timely intervention and treatment planning. In recent years, the integration of deep learning techniques into medical imaging has revolutionized the field of ophthalmology. This study explores the application of deep learning algorithms for the classification of retinal diseases using high-resolution retinal images. This study presents a comprehensive and innovative framework for retinal disease classification using deep learning, addressing critical technical challenges and ethical considerations. The developed model represents a significant step towards enhancing the accuracy and efficiency of retinal disease diagnosis, ultimately improving patient outcomes and the quality of eye care..

KEYWORDS

Retinal movement disease discrimination deep learning CNN classifier.

Cite This Article

Prof Sayali Shivarkar1, Unmesh Bhunia2, Shrihari Ambekar3, Samarth Walunj4, Soham Songire5, "Retinal Diseases Classification using Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 2, Feb 2025. https://doi.org/10.55041/IJSREM41734

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Publication Date

2/21/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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