INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Diabetic retinopathy (DR) is a leading cause of blindness in
working-age adults and requires early diagnosis and
intervention to prevent vision loss. In this study, we present
automatic diagnosis using deep learning techniques,
especially efficient network architecture, to classify retinal
images into different stages of DR. It is a system that
facilitates timely treatment and management of patients by
increasing the efficiency and accuracy of DR diagnosis. The
experimental results demonstrate the effectiveness of the
proposed method in accurately identifying DR stages and
have high potential for implementation in clinical practice.
This study discusses the basics of diabetes, its prevalence,
problems, and wisdom on early detection and classification of
diabetic retinopathy. This study also discusses AI-based
technologies such as machine learning and deep learning.
New research areas such as adaptive learning,
interdisciplinary learning, and artificial intelligence are also
being explored using various communication methods to
explain diabetic retinopathy. Current literature, screening,
efficacy evaluations, biomarkers of diabetic retinopathy,
possible complications and list of ophthalmic complications,
and future implications are discussed. There is no other
information available from the authors to describe the current
status of the PRISMA approach and the experience on which
it is based.
Keywords:
Retinopathy in Diabetes, Fundus representation, Convolutional Neural Architecture, Image categorization.
Cite Article:
"Diagnosis of Diabetic Retinopathy Using Deep Neural Network ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.e349-e353, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404439.pdf
Downloads:
00032
ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Facebook Twitter Instagram LinkedIn