Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs
| Autor: | May Phu Paing, Kazuhiko Hamamoto, Toan Huy Bui |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Feature extraction Decision tree 02 engineering and technology lcsh:Technology tooth decay lcsh:Chemistry 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering General Materials Science dental radiographs Instrumentation lcsh:QH301-705.5 Complement (set theory) caries Fluid Flow and Transfer Processes business.industry lcsh:T Process Chemistry and Technology Deep learning General Engineering deep learning Pattern recognition 030206 dentistry lcsh:QC1-999 Computer Science Applications Random forest Support vector machine features extraction machine learning lcsh:Biology (General) lcsh:QD1-999 Feature (computer vision) lcsh:TA1-2040 020201 artificial intelligence & image processing Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 11, Iss 2005, p 2005 (2021) Applied Sciences Volume 11 Issue 5 |
| ISSN: | 2076-3417 |
| Popis: | Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous state-of-the-art and shows promising results when none of the measured factors is less than 90% therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals. |
| Databáze: | OpenAIRE |
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