Cholesterol Detection Through Iris Using Daugman's and GLCM Based on K-Means Clustering

Cholesterol Detection Through Iris Using Daugman's and GLCM Based on K-Means Clustering

Cholesterol is a disease that is influenced by fat deposits originating from the liver. Detection of cholesterol disease can be known through blood tests, urine checks and visually the iris of the human eye. Cholesterol detection through the iris can be implemented using image processing techniques,...

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Bibliographic Details
Published in:2022 International Seminar on Application for Technology of Information and Communication (iSemantic) pp. 474 - 478
Main Authors: Fadilla, Neza Aemal, Sakti, Mohamad Lathif Puja, Setyaningsih, Nita, Zhafran, Naufal, Pramudyawardhana, Taufik Aulia, Shelomita, Viki Arri, Ideastari, Nukat Alvian, Sari, Christy Atika, Rachmawanto, Eko Hari, Fahmi, Amiq
Format: Conference Proceeding
Language:English
Published: IEEE 17-09-2022
Subjects:
Cholesterol
Daugman's
Feature extraction
GLCM
Image segmentation
Image texture
Iris
K-Means Clustering
Liver
Pattern classification
Seminars
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Summary:Cholesterol is a disease that is influenced by fat deposits originating from the liver. Detection of cholesterol disease can be known through blood tests, urine checks and visually the iris of the human eye. Cholesterol detection through the iris can be implemented using image processing techniques, especially in image segmentation. Input-based image segmentation on feature extraction and pattern classification has been applied in this article. GLCM is a feature extraction technique that is commonly used to sharpen image textures to make the classification process easier. In this article, K-Means have been selected to carry out the classification process. To improve accuracy, the original image has been preprocessed using grayscalling, noise removal, image contrast enhancement and cropping. The experimental results have obtained 100% accuracy.
ISBN:9781665488372
1665488379
DOI:10.1109/iSemantic55962.2022.9920428