FEATURE EXTRACTION AND K-MEANS CLUSTERING APPROACH TO CLASSIFY THE COVID-19 LUNG CT-SCAN IMAGE
(1) Department of Computer Science, Institut Teknologi Nasional Malang, Malang City, East Java
Corresponding Author
Abstract
Feature extraction is the most important step in the classification process. Feature extraction is a method to obtain some statistical features about the image. The level of accuracy in the classification depends on the feature extraction. For detecting COVID-19, there are many features that can be used to classify them, including morphological feature extraction, first-order and second-order textures (GLCM). In this research, some features are used such as eccentricity, metric, mean, variance, skewness, contrast, correlation, energy, and homogeneity, which are then classified by the K-Means Method. The morphological feature data for cluster 1 is 98 data points and cluster 2 is 32 data points. The first-order texture feature data for cluster 1 is 88 data points, and cluster 2 is 42 data points. The last one uses GLCM data for cluster 1, and there are 75 data points, while cluster 2 has 55. From the calculation of accuracy, sensitivity, specificity, precision, and recall, the highest value is 50% for first-order texture extraction data, while the morphological feature extraction and GLCM data are 49.23% and 47.69%.
Keywords
References
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DOI: 10.56327/ijiscs.v5i3.1109
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