Classifying cyst and tumor lesion using Support Vector Machine based on
dental panoramic images texture features
a Electrical Engineering Department, Hasanuddin
University, Makassar, 90245, Indonesia
b Dentistry Department, Airlangga University, Surabaya, Indonesia
b Dentistry Department, Airlangga University, Surabaya, Indonesia
Abstract
Dental
radiographs are essential in diagnosing the pathology of the jaw. However,
similar radiographic appearance of jaw lesions causes difficulties in
differentiating cyst from tumor. Therefore, we conducted a development of
computer-aided classification system for cyst and tumor lesions in dental
panoramic images. The proposed system consists of feature extraction based on
texture using the first-order statistics texture (FO), Gray Level Co-occurrence
Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). In this work, there
were thirty three features which were classified using Support Vector Machine
(SVM) based classification. The result shows that differentiation of cyst from
tumor lesions can achieve accuracy up to 87.18% and Area Under the Receiver
Operating Characteristic (AUC) curve up to 0.9444. When using the number of
features used as predictors, the highest accuracy obtained were 8462% using FO,
61.54% using GLCM, 76.92% using GLRLM, 84.62% using the combination of FO and
GLCM, 87.18% using the combination of FO and GLRLM, 75.56% using the
combination of GLCM and GLRLM, and 87.18% using the combination of FO, GLCM and
GLRLM. The highest AUC value was 0.9361 using FO, using GLCM was 0.8667, using
GLRLM was 0.8722, using the combination of FO and GLCM was 0.9278, using the
combination of FO and GLRLM was 0.9444, using the combination of GLCM and GLRLM
was 0.8417, and using the combination of FO, GLCM and GLRLM was 0.9278. Based
on the AUC value, the level of accuracy of this prediction can be categorized
as 'Excellent'.
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