The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images
Vo Truong Nhu Ngoc,
Agwu Chinedu Agwu,
Le Hoang Son,
Tran Manh Tuan,
Cu Nguyen Giap,
Mai Thi Giang Thanh,
Hoang Bao Duy,
Tran Thi Ngan
Affiliations
Vo Truong Nhu Ngoc
School of Odonto–Stomatology, Hanoi Medical University, Hanoi 010000, Vietnam
Agwu Chinedu Agwu
Information and Communication Technology Department, University of Science and Technology, Hanoi 010000, Vietnam
Le Hoang Son
VNU Information Technology Institute, Vietnam National University, Hanoi 010000, Vietnam
Tran Manh Tuan
Faculty of Information Technology, Thuyloi University, 175 Tay Son, Dong Da, Hanoi 010000, Vietnam
Cu Nguyen Giap
Faculty of Management Information System & E-commerce, Thuongmai University, Hanoi 010000, Vietnam
Mai Thi Giang Thanh
School of Odonto–Stomatology, Hanoi Medical University, Hanoi 010000, Vietnam
Hoang Bao Duy
School of Odonto–Stomatology, Hanoi Medical University, Hanoi 010000, Vietnam
Tran Thi Ngan
Faculty of Information Technology, Thuyloi University, 175 Tay Son, Dong Da, Hanoi 010000, Vietnam
In dental diagnosis, recognizing tooth complications quickly from radiology (e.g., X-rays) takes highly experienced medical professionals. By using object detection models and algorithms, this work is much easier and needs less experienced medical practitioners to clear their doubts while diagnosing a medical case. In this paper, we propose a dental defect recognition model by the integration of Adaptive Convolution Neural Network and Bag of Visual Word (BoVW). In this model, BoVW is used to save the features extracted from images. After that, a designed Convolutional Neural Network (CNN) model is used to make quality prediction. To evaluate the proposed model, we collected a dataset of radiography images of 447 patients in Hanoi Medical Hospital, Vietnam, with third molar complications. The results of the model suggest accuracy of 84% ± 4%. This accuracy is comparable to that of experienced dentists and radiologists.
