DETEKSI PENYAKIT DIABETIC RETINOPATHY PADA CITRA RETINA MENGUNAKAN RETINANET DENGAN BACKBONE RESNET 101

YAP, SAMUEL and Erwin, Erwin (2022) DETEKSI PENYAKIT DIABETIC RETINOPATHY PADA CITRA RETINA MENGUNAKAN RETINANET DENGAN BACKBONE RESNET 101. Undergraduate thesis, Sriwijaya University.

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Abstract

The retina is the thinnest cell part on the inside of the eye which has 2 cell parts, namely rod and cone cells. The retina consists of two structures: the blood vessels and the macula. Diabetic Retinopathy (DR) is a disease that occurs due to too high sugar levels in the blood so that it can clog blood vessels and stop blood supply. Diabetic retinopathy attacks the vision, more precisely occurs in the retinal blood vessels and can cause vision problems. Residual Network (ResNet) is an artificial neural network created to anticipate low accuracy. For this reason, ResNet is used to create artificial neural networks with deep layers to get high accuracy. This study presents a method for disease detection in retinal images using the STARE dataset and RETINANET architecture with backbone ResNet-101. The data annotation process is carried out to identify the characteristics of the disease in retinal images using the labelimg application. The proposed method with the STARE dataset gets an average value with a precision for diabetic retinopathy of 84.706% , average precision of 74.48% and intersection over union of 84.7%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Diabetic Retinopathy (DR), RETINANET, Resnet-101, retinal image, detection
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	student samuel yap samuel yap
Date Deposited:	07 Oct 2022 07:55
Last Modified:	07 Oct 2022 07:55
URI:	http://repository.unsri.ac.id/id/eprint/80570

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