Efficient Screening of Diseased Eyes based on Fundus Autofluorescence Images using Support Vector Machine

Manne, S. R. and Vupparaboina, K. K. and Gudapati, G. C. and Peddoju, R. A. and Konkimalla, C. P. and Bashar, S. B. and Goud, A. and Chhablani, J. and Jana, Soumya (2021) Efficient Screening of Diseased Eyes based on Fundus Autofluorescence Images using Support Vector Machine. In: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021, 27 July 2021 through 30 July 2021, Virtual, Online.

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Abstract

A variety of vision ailments are associated with the foveal region of the eye. In current clinical practice, the ophthalmologist manually detects potential presence of such ailments based on fundus autofluorescence (FAF) images, and hence diagnoses the disease, when relevant. However, in view of the general scarcity of ophthalmologists relative to the large number of subjects seeking eyecare, especially in remote regions, it becomes imperative to develop methods to direct expert time and effort to medically significant cases. To serve the interest of both the ophthalmologist and the potential patient, we plan a screening step, where healthy and diseased eyes are algorithmically differentiated with limited input from only optometrists who are relatively more abundant in number. Specifically, an early treatment diabetic retinopathy study (ETDRS) grid is placed by an optometrist on each FAF image, based on which sectoral statistics are automatically collected. Using such statistics as features, healthy and diseased eyes are proposed to be classified by training an algorithm using available medical records. In this connection, we consider support vector machine (SVM) with linear as well as radial basis function (RBF) kernel, and observe satisfactory performance of both variants. Among those, we recommend the latter in view of its slight superiority in terms of classification accuracy (90.55% at a standard training-to-test ratio of 80:20), and practical class-conditional costs. © 2021 IEEE

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IITH Creators:

IITH Creators	ORCiD
Jana, Soumya	https://orcid.org/0000-0002-5880-4023

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

The work was partly supported by Grant BT/PR16582/BID/7/667/2016, Department of Biotechnology (DBT), Ministry of Science and Technology, the Government of India. S. R. Manne thanks the Ministry of Electronics and Information Technology (MeitY), the Government of India, for fellowship grant under Visvesvaraya PhD Scheme.

Uncontrolled Keywords:

Early treatment diabetic retinopathy study (ETDRS) grid; Fundus autofluorescence (FAF); Monte Carlo cross validation (MCCV); Support vector machine (SVM)

Subjects:

Electrical Engineering

Divisions:

Department of Electrical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

18 Aug 2022 15:00

Last Modified:

18 Aug 2022 15:00

URI:

http://raiithold.iith.ac.in/id/eprint/10215