Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features

• Main Authors: Ingrid Nurtanio, -, Eha Renwi Astuti, -, I Ketut Eddy Purnama, -, Mochamad Hariadi, -, Mauridhi Hery Purnomo, -
• Format: Article PeerReviewed
• Language: English; English; Indonesian; English
• Published: International Association of Engineers 2013
• Subjects: R Medicine; RK Dentistry
• Online Access: https://repository.unair.ac.id/118053/1/4.%20Classifiying%20Cyst%20and%20Tumor%20lesion%20using%20Support%20Vector%20Machine%20Based%20on%20Dental%20Panoramic%20Images%20Texture%20Features..pdf; https://repository.unair.ac.id/118053/7/4.%20Classifying%20Cyst%20and%20Tumor%20Lesion%20Using%20Support%20Vector%20Machine%20Based%20on%20Dental%20Panoramic%20Images%20Texture%20Features_2.pdf; https://repository.unair.ac.id/118053/3/4.%20Classifiying%20Cyst%20and%20Tumor%20lesion%20using%20Support%20Vector%20Machine%20Based%20on%20Dental%20Panoramic%20Images%20Texture%20Features.pdf; https://repository.unair.ac.id/118053/9/3..%20Classifiying%20Cyst%20and%20Tumor%20lesion.pdf; https://repository.unair.ac.id/118053/
• Institution: Universitas Airlangga

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’.