KLASIFIKASI CITRA RETINA DALAM MENENTUKAN PENYAKIT GLAUKOMA MENGGUNAKAN ARSITEKTUR VISION TRANSFORMER

PUTRI, DEWI LESTARI DWI and Desiani, Anita and Andriani, Yuli (2022) KLASIFIKASI CITRA RETINA DALAM MENENTUKAN PENYAKIT GLAUKOMA MENGGUNAKAN ARSITEKTUR VISION TRANSFORMER. Undergraduate thesis, Sriwijaya University.

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Abstract

Glaucoma is the second leading cause of blindness in the world. Early detection of glaucoma needs to be done to reduce the risk of blindness. So, we need a technology to predict and classify glaucoma. Therefore, this study performed retinal image classification to determine glaucoma. Glaucoma can be classified to 3 classes, there are advanced glaucoma, early glaucoma, and normal control. Deep learning is often used to perform various tasks in machine learning such as classification. One of the learning methods in classification is Vision Transformer (ViT). The advantage of ViT is its small memory usage with excellent results in performing classification tasks. In this study, retinal image classification was performed to determine glaucoma disease using the ViT architecture. The research stages are data collection, data pre-processing, training, testing, and performance evaluation as well as analysis and interpretation. The study used retinal image data sourced from the Harvard Dataverse with the performance evaluation values for accuracy, sensitivity, specificity, F1-score, and Cohen's kappa were 96.74%, 95.10%, 97.57%, 95.10%, and 92.46%, respectively. Based on the results obtained, it can be concluded that the Vision Transformer architecture is very good in performing the task of classifying retinal images to determine glaucoma disease.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi multiclass, evaluasi, Vision Transformer
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Users 23674 not found.
Date Deposited:	25 Aug 2022 08:23
Last Modified:	25 Aug 2022 08:23
URI:	http://repository.unsri.ac.id/id/eprint/77816

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