Classification of Multi-retinal Disease Based on Retinal Fundus Image Using Convolutional Neural Network

A. Vanita Sharon and G. Saranya
Additional contact information
A. Vanita Sharon: SRM Institute of Science and Technology, Big Data Analytics
G. Saranya: SRM Institute of Science and Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1009-1016 from Springer

Abstract: Abstract Retinal disease often refers to retinal vascular disease is currently growing tremendously in the field of ophthalmology which is to be diagnosed early to prevent from blindness. In recent days, many retinal diseases that cause damage to retina due to abnormal blood flow. In this paper, multiclass classification is been proposed for four classes of diseases such as Arteriosclerotic Retinopathy, Background Diabetic Retinopathy, Choroidal Neovascularization, Hypertensive Retinopathy. The proposed model uses convolutional neural network which extracts its own features and classify them when compared to other classification methods. The system uses ALEXNET architecture which is deeper with more filters that can extract the features of the image automatically and classifies them to predict the class of disease it belongs to. The model is trained over the data which is been collected from STRARE database. As a result, the model is able to achieve the prediction of test case and classifies the disease which brings betterment in diagnosis of Retinal Diseases and avoids from blindness.

Keywords: Deep learning; Multiclass classification; Convolutional neural networks (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_102

Ordering information: This item can be ordered from
http://www.springer.com/9783030418625

DOI: 10.1007/978-3-030-41862-5_102

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().