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Automatic detection of diabetic retinopathy retinal images using artificial neural network

Syamimi Mardiah, Shaharum and Nurul Hajar, Hashim and Nurhafizah, Abu Talip and Mohamad Shaiful, Abdul Karim and Ahmad Afif, Mohd Faudzi (2019) Automatic detection of diabetic retinopathy retinal images using artificial neural network. In: Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 27-28 September 2018 , Universiti Malaysia Pahang. pp. 495-503., 538. ISBN 978-981-13-3708-6 (Online)

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Abstract

The Diabetic Retinopathy (DR) is a critical vascular disorder that can cause a permanent blindness. Thus, the early recognition and the treatment are required to avoid major vision loss. Nowadays manual screening is done however, they are very incompetent to large image database of patients and most importantly they are very time consuming. Besides, it required skilled professionals for the diagnosis. Automatic DR diagnosis systems can be as an optional method to the manual methods as they can significantly reduce the manual screening process labor. Screening conducted over a larger population can become effective if the system can distinguish between normal and abnormal cases, as a replacement for the manual examination of all images. Hence, the development of an Automated Diabetic Retinopathy detection systems has been recognized in the current times. This study has successfully developed an automated detection system for proliferative diabetic retinopathy symptoms using an artificial neural network with two types of feature used; mean of pixel and area of the pixel. The highest accuracy of this system is 90% with 30 hidden neurons in the neural network trained for all features. The results clearly show that the proposed method is effective for detection of Diabetic Retinopathy.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Part of the Lecture Notes in Electrical Engineering book series
Uncontrolled Keywords: Diabetic retinopathy; Artificial neural network; Automated detection
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 09 Dec 2019 03:44
Last Modified: 09 Dec 2019 03:44
URI: http://umpir.ump.edu.my/id/eprint/25047
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