.png){kind=icon}
返回
Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3150358.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Panoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and inexperience, manual diagnosis may cause unnoticeable dental caries. Thus, computer-based intelligent vision systems supported by machine learning and image processing techniques are needed to prevent these negativities. This study proposed a novel approach for the automatic diagnosis of dental caries based on periapical images. The proposed procedure used a multi-input deep convolutional neural network ensemble (MI-DCNNE) model. Specifically, a score-based ensemble scheme was employed to increase the achievement of the proposed MI-DCNNE method. The inputs to the proposed approach were both raw periapical images and an enhanced form of it. The score fusion was carried out in the Softmax layer of the proposed multi-input CNN architecture. In the experimental works, a periapical image dataset (340 images) covering both caries and non-caries images were used for the performance evaluation of the proposed method. According to the results, it was seen that the proposed model is quite successful in the diagnosis of dental caries. The reported accuracy score is 99.13%. This result shows that the proposed MI-DCNNE model can effectively contribute to the classification of dental caries.
Keyword:
Dentistry
Convolutional neural networks
X-ray imaging
Teeth
Feature extraction
Diseases
Training
Dental caries detection
score-based fusion
deep convolutional neural network
classification
periapical images
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
From Vulnerability to Empowerment: Critical Reflections on Canada’s Engagement with Refugee Policy.png)
LawsOAAI
LawsIF0
OAAI分享收藏分享收藏The power of obfuscation techniques in malicious JavaScript code: A measurement study恶意JavaScript代码中混淆技术的功能: 一项测量研究
OAAI
OAAI分享收藏Classification of caries in third molars on panoramic radiographs using deep learningSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容