Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection

Title

Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection

Creator

Benjamin A.; Wahid F.F.; Jenefa J.

Description

INTRODUCTION: Diabetic retinopathy (DR) is one of the consequences of diabetes which if untreated may lead to loss of vision. Generally, for DR detection, retinal images are obtained using a traditional fundus camera. A recent trend in the acquisition of eye fundus images is the usage of smartphones to acquire images. OBJECTIVES: This paper focuses on the study of existing works which incorporated smartphones for obtaining fundus images and various devices available in the market. Also, the common datasets used for carrying out DR detection using smartphone-based fundus images as well as the classification models used for the diagnosis of DR are explored. METHODS: A search of information was carried out on articles based on DR detection from fundus images published in the state-of-the-art literatures. RESULTS: Majority of the works uses SBFI devices like 20D lens, EyeExaminer etc. to obtain fundus image. The common databases used for the study are EyePACS, Messidor, etc. and the classification models mostly rely on deep learning frameworks. CONCLUSION: The use of smartphones for capturing fundus images for DR detection are explored. Smartphone devices, datasets used for the study and currently available classification models for SBFI based DR detection are discussed in detail. This paper portrays various approaches currently being employed in SBFI based DR detection. 2023 A. Benjamin et al.

Source

EAI Endorsed Transactions on Pervasive Health and Technology, Vol-9

Date

2023-01-01

Publisher

European Alliance for Innovation

Subject

deep learning; diabetic retinopathy; Fundus imaging; smartphone-based fundus imaging

Coverage

Benjamin A., Department of Artificial Intelligence and Machine Learning, CMR Institute of Technology, Karnataka, Bengaluru, 560037, India; Wahid F.F., Department of Information Technology, Kannur University, Kerala, Kannur, 670567, India; Jenefa J., Department of Computer Science and Engineering, Christ (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560074, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 24117145

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.4108/eetpht.9.4376

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178320866&doi=10.4108%2feetpht.9.4376&partnerID=40&md5=7d67b3d8f313f05c0b1a0e8b95ea96c3