Implementation of Extreme Learning Machine for Classification of Retina Ablasio Results on Retina Fundus Images
DOI:
https://doi.org/10.30865/klik.v3i4.615Keywords:
Retinal Detachment Disease; simple thresholding; Gray Level Co-Occurance Matrix; Extreme Learning MachineAbstract
Retinal detachment is a disorder of the retina of the eye that results in detachment of the retina from its supporting tissue. Retinal detachment can lead to permanent vision loss (blindness). Factors that cause retinal detachment with increasing severity are aging, genes, high myopia, severe eye injury, cataract surgery, and ocular inflammation. Examination in diagnosing retinal detachment through fundoscopy to observe the presence of very pale retinal blood vessels that are detached with a white appearance in the form of vitreous, wavy folds, and bends at the edge of the retina. However, the diagnostic examination is carried out manually by ophthalmologists so that it can lead to unclear observations and possibly fatal visual disturbances. For this reason, a new alternative is needed in classifying retinal detachments. Therefore, this study implements the Extreme Learning Machine (ELM) method in the retinal detachment classification process. The stages used in the research before being classified are resize, green channel, and contrast as the pre-processing stage and simple thresholding as the image segmentation stage and Gray Level Co-Occurrence Matrix (GLCM) as the feature extraction stage. In the final stage, the image will be classified with Extreme Learning Machine. This study uses retinal fundus images totaling 178 images which are divided into 133 images as data latih and 45 images as test data. The results of this study were able to classify retinal detachments with an accuracy of 91%.
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