BIRALAB
Bài báo tạp chí2020

The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

Kết hợp mạng nơ-ron tích chập thích nghi và túi từ thị giác để tự động chẩn đoán biến chứng răng số 8 trên ảnh X-quang răng

Tác giả

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

Tên thành viên BIRALAB được in đậm và liên kết tới trang cá nhân.

Tóm tắt

<jats:p>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.</jats:p>