Title: Feature analysis for fundus image classification of multi-retinal diseases

Authors: Widhia K.Z. Oktoeberza

Addresses: Department of Informatics, Faculty of Engineering, Universitas Bengkulu, Jl. W.R Supratman, Kandang Limun, Bengkulu 38371A, Indonesia

Abstract: Retinal diseases affect the vital eye tissue, which can reduce eye vision and even cause blindness if left undiagnosed and untreated. Some retinal diseases can be prevented and even treated properly to return the lost vision by conducting early detection. A scheme to classify multi-retinal diseases is proposed in this study; specifically diabetic retinopathy (DR), age-related macular degeneration (ARMD), and media haze (MH). The process is starting by extracting some features consisting of statistical and texture features, which are undergone in 500 fundus images taken from the RFMiD dataset. Thereupon, these features were classified based on the MLP classifier. Based on that classification process, the accuracy rate of DR, ARMD, and MH classifications achieved 84.2%, 93.2%, and 89.4%, respectively. These achievements show that the proposed scheme effectively classifies multi-retinal diseases and has the potential to assist ophthalmologists in early detecting the appearance of retinal diseases for preventing the worst effect.

Keywords: retinal diseases; feature analysis; image classification; fundus images.

DOI: 10.1504/IJMEI.2024.140807

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.5, pp.466 - 475

Received: 13 Dec 2021
Accepted: 15 May 2022
Published online: 03 Sep 2024 *

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