Diabetic Retinopathy Severity Level Detection Using Convolution Neural Network

Firmansyah, Achmad Dinofaldi (2023) Diabetic Retinopathy Severity Level Detection Using Convolution Neural Network. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

Diabetic retinopathy is a common complication of diabetes mellitus, leading to damage and blockage of retinal blood vessels. Early and accurate detection of diabetic retinopathy severity levels is crucial for timely treatment and prevention of blindness. Diagnostic methods rely on manual examination and human interpretation, resulting in slower and less efficient treatment processes. As a branch of artificial intelligence, computer vision offers a potential solution to analyze retinal images quickly and accurately. The developed system employs image processing techniques and a CNN based classification model to detect and classify the severity levels of diabetic retinopathy. By providing an automated and efficient approach, the system aims to assist doctors and optometrists in making informed decisions and reducing subjectivity in diagnosis. Early detection through this system can facilitate prompt treatment and improve patient outcomes. The developed system achieves promising results through experimentation and testing with various datasets, with accuracy ranging from 80% to 97%. This project's integration of artificial intelligence, machine learning, and image processing technologies demonstrates their potential in healthcare applications, particularly in diabetic retinopathy diagnosis.

Item Type: Thesis (Undergraduate)
Contributors:
ContributionContributorsNIDN/NIDK
Thesis advisorFitri, Zilvanhisna EmkaNIDN0002039203
Uncontrolled Keywords: diabetic retinopathy, computer vision, Convolution Neural Network (CNN), image processing
Subjects: 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 458 - Teknik Informatika
Divisions: Jurusan Teknologi Informasi > Prodi D4 Teknik Informatika > Tugas Akhir
Depositing User: Achmad Dinofaldi Firmansyah
Date Deposited: 12 Jul 2023 03:03
Last Modified: 12 Jul 2023 03:03
URI: https://sipora.polije.ac.id/id/eprint/24608

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