Universiti Teknologi Malaysia Institutional Repository

Convolution neural network model for fundus photograph quality assessment

Mohammed Sheet, Sinan S. and Tan, Tian-Swee and As’ari, Muhammad Amir and Wan Hitam, Wan Hazabbah and Ngoo, Qi Zhe and Sia, Joyce Sin Yin and Ling, Kelvin Chia Hiik (2022) Convolution neural network model for fundus photograph quality assessment. Indonesian Journal of Electrical Engineering and Computer Science, 26 (2). pp. 915-923. ISSN 2502-4752

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Official URL: http://dx.doi.org/10.11591/ijeecs.v26.i2.pp915-923

Abstract

The excellent quality of color fundus photograph is crucial for the ophthalmologist to process the correct diagnosis and for convolutional neural network (CNN) models to optimize output classification. As a result of main causes as acquire devises efficiency and experience of a physician most fundus photographs can have uneven illuminance, blur, and bad contrast, in addition to micro-features of retinal diseases, which need to force their contrast. Fundus photograph quality assessment method is proposed to find out the perfect enhanced color fundus Technique in fundoscopy photographs-based CNN model. Five photograph quality measurements, in addition to five CNN metrics, were used as standard in this study. In this research innovative approach combining photograph quality measurement and CNN metrics analysis is proposed to find out the best enhance method that is set for the multiclass CNN model. The contrast enhancement techniques are evaluated using 267 color fundus photographs divided into three retina diseases cases were downloaded from the open-source database "FIGSHARE". The study outcome showed that the presented system (single-CNN) can determine well the contrast enhancement method, as well as the low-quality fundus photograph then it can boost CNN metrics to achieve superior.

Item Type:Article
Uncontrolled Keywords:CNN metrics, CNN model, Contrast enhancement, Fundus photograph, Photograph quality assessment
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:104458
Deposited By: Widya Wahid
Deposited On:08 Feb 2024 08:03
Last Modified:08 Feb 2024 08:03

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