Diabetic retinopathy detection using convolutional neural networka study

Title

Diabetic retinopathy detection using convolutional neural networka study

Creator

Wahid F.F.; Raju G.

Description

Detection and classification of Diabetic Retinopathy (DR) is a challenging task. Automation of the detection is an active research area in image processing and machine learning. Conventional preprocessing and feature extraction methods followed by classification of a suitable classifier algorithm are the common approaches followed by DR detection. With the advancement in deep learning and the evolution of Convolutional Neural Network (CNN), conventional preprocessing and feature extraction steps are rapidly being replaced by CNN. This paper reviews some of the recent contributions in diabetic retinopathy detection using deep architectures. Further, two architectures are implemented with minor modifications. Experiments are carried out with different sample sizes, and the detection accuracies of the two architectures are compared. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

Source

Lecture Notes in Networks and Systems, Vol-132, pp. 127-133.

Date

2021-01-01

Publisher

Springer

Subject

Convolutional neural network; Deep learning; Diabetic retinopathy

Coverage

Wahid F.F., Department of Informtion Technology, Kannur University, Kerala, India; Raju G., Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 23673370

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-15-5309-7_13

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369011&doi=10.1007%2f978-981-15-5309-7_13&partnerID=40&md5=411621492f3ce8dfdfddd55c8773a769