Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

Toan Huy Bui,May Phu Paing,Kazuhiko Hamamoto

doi:10.3390/app11052005

Toan Huy Bui, May Phu Paing + Show 1 more

Open Access

https://doi.org/10.3390/app11052005

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Abstract

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.

Highlights

Oral health plays a main role in people’s overall health and quality throughout their lifetime, regardless of nationality, region, or religion
Radiographs are usually shown in grayscale images or sometimes in color images; color radiographs require significant investment, which provides a barrier to entry for most hospitals, especially hospitals in low-income countries; to account for this, we focused on grayscale radiographs
We present precision or positive predictive value (PPV), negative predictive value (NPV), f1score, the area under the curve (AUC), and processing time to give a comprehensive view about the advantage of the proposed method and for other research reference purposes

Summary

Introduction

Oral health plays a main role in people’s overall health and quality throughout their lifetime, regardless of nationality, region, or religion. It is healthy conditions without mouth and facial pain, oral and throat cancer, oral infection and sores, periodontal (gum) diseases, tooth decay, tooth loss, and disorders that limit an individual’s capacity in biting, chewing, speaking, and psychosocial wellbeing. Known as tooth decay or oral cavities, is the most common disease that affects the quality of life worldwide. Dental treatment costs 5% of total health spending and is generally a 20% out-of-pocket expenditure in many developed countries. The condition seems to be worse in most developing countries where people cannot afford oral health treatment services. Most caries conditions are treatable and preventable in the early stage, 4.0/)

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