Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features
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 denta...
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| 總結: | 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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