Citation

Loh, Nicole Kai Ning and Wee, Yit Yin (2025) Diagnosis of eye diseases using Support Vector Machine with Bayesian optimization. In: 2025 8th International Conference on New Media Studies (CONMEDIA), 14-17 October 2025, Malacca, Malaysia.

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Abstract

Eye diseases have become a significant public concern, particularly following the COVID-19 pandemic in 2020. Daily activities such as studying and working increasingly rely on electronic devices, which emit blue light. This light scatters within the eyes, creating additional visual noise. However, there is public concern regarding diagnostic accuracy, as human interpretation is prone to errors. Such errors may result in blindness or severe ocular damage. Therefore, this research proposes the diagnosis of eye diseases using Optical Coherence Tomography (OCT) images. After pre-processing the images, features from the images are extracted using the Principal Component Analysis (PCA). These features are useful for classifying the retinal diseases. For retinal disease classification, performance is enhanced by applying Support Vector Machine (SVM) with the Bayesian optimization. The results are compared against grid search and random search approaches. All the techniques are implemented in Python.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Bayesian optimization, Eye diseases, Optical Coherence Tomography (OCT), Principal Component Analysis (PCA), Support Vector Machine (SVM)
Subjects:	Q Science > QP Physiology > QP(901)-(981) Experimental pharmacology
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	20 Apr 2026 04:35
Last Modified:	20 Apr 2026 04:35
URII:	http://shdl.mmu.edu.my/id/eprint/15789

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