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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
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| Paper Title: | Quantification of Retinal Tissue Damage |
| Authors Name: | Sudeshna Bhushan , Yashaswini Shah , Ishan Bhandari , Ritvika Singh Rathore , Dr. Prasad G R |
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| Author Reg. ID: |
IJRTI_187012
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| Published Paper Id: | IJRTI2305232 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | Retina is the outer lining of the human eye where image formation takes place. Any threat to the retina causes severe eye defects and may lead to complete blindness. During a defect the retina gets distorted. To measure the severity of a disease we need to determine different retinal tissue damages. These damages must be quantified to make useful predictions. Here we attempt to quantify retinal tissue damage through various image processing techniques. To verify our estimate we applied machine learning algorithms to create a classifier for the detection of diabetic retinopathy and macular edema disease. Diabetic retinopathy & Macular Edema are diseases prone to diabetic people. They cause progressive damage to the retina of the eye. DR is the leading cause of blindness in the working-age population of the developed world. It is estimated to affect over 93 million people. Diabetic Retinopathy is an active research area. A lot of research has been done in the last few years. Computer scientists and medical researchers have developed many algorithms for the automatic detection of eye diseases, though accuracy has never been very great. Researchers have been trying new features and new algorithms to improve further. Diabetic Retinopathy (DR) is a complication caused by diabetes that affects the human eye. It is caused by the mutilation of the blood vessels of the light-sensitive tissue at the back of the human retina. It’s the most recurrent cause of blindness in the working-age group of people and is highly likely when diabetes is poorly controlled. Although, methods to detect Diabetic Retinopathy exist, they involve manual examination of the retinal image by an Ophthalmologist. The Proposed approach of DR detection aims to detect the complication in an automated manner using Deep Learning. |
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| Cite Article: | "Quantification of Retinal Tissue Damage ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.2225 - 2234, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305232.pdf |
| Downloads: | 000205289 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2305232
Registration ID:187012
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 2225 - 2234 Country: Bengaluru , Bangalore , India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2305232 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2305232 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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