Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image

Sebatubun, M. M. and Haryawan, Cosmas and Windartac, B. (2018) Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image. Journal of Experiental & Theoretical Artificial Intelligence, 30 (2). pp. 1-13. ISSN 0952-813X

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Abstract

Lung cancer causes a high mortality rate in the world than any other cancers. That can be minimised if the symptoms and cancer cells have been detected early. One of the techniques used to detect lung cancer is by computed tomography (CT) scan. CT scan images have been used in this study to identify one of the lesion characteristics named ground glass opacity (GGO). It has been used to determine the level of malignancy of the lesion. There were three phases in identifying GGO: image cropping, feature extraction using grey level co-occurrence matrices (GLCM) and classification using Naïve Bayes Classifier. In order to improve the classification results, the most significant feature was sought by feature selection using gain ratio evaluation. Based on the results obtained, the most significant features could be identified by using feature selection method used in this research. The accuracy rate increased from 83.33% to 91.67%, the sensitivity from 82.35% to 94.11% and the specificity from 84.21% to 89.47%. KEYWORDS: GLCM; ground glass opacity; Naïve Bayes classifier

Item Type: Article
Additional Information: Penulis: M. M. Sebatubun, C. Haryawan, dan B. Windarta
Uncontrolled Keywords: GLCM; ground glass opacity; Naïve Bayes classifier
Subjects: A Karya Umum (General) > Ilmu Komputer (Computer Science) > Teknologi Informasi
Divisions: Jurnal (Journal)
Depositing User: Titis Pratiwi
Date Deposited: 03 Apr 2023 01:27
Last Modified: 03 Apr 2023 01:27
URI: http://eprints.utdi.ac.id/id/eprint/9946

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