scholarly journals Automatic Tooth Segmentation and Classification in Dental Panoramic X-ray Images

2020 ◽  
Author(s):  
Shuxu Zhao ◽  
QING LUO ◽  
Changrong Liu
Keyword(s):  
Recognition Task ◽  
Recall Rate ◽  
X Ray ◽  
Learning Framework ◽  
Tooth Position ◽  
Data Engineering ◽  
Segmentation Task ◽  
Instance Segmentation ◽  
Tooth Segmentation

Abstract Background: The information of tooth shape, type and tooth position plays an important role in the understanding of pathological features in dental X-ray films. It is of great significance to realize the accurate tooth segmentation and tooth classification of dental panoramic X-ray images for the construction of an intelligent dental diagnosis system.At present, the segmentation results of teeth are relatively rough, and most methods realize tooth recognition and segmentation as independent tasks, ignoring the parameter sharing between the two tasks. Therefore, an instance segmentation method which can realize tooth recognition and tooth segmentation at the same time is proposed. Methods: In model designing, the Mask R-CNN, an instance segmentation model , is adopted, which includes classification branches and segmentation branches. The classification branch can be used to complete the tooth recognition task and the segmentation branch to complete the tooth segmentation task. On this basis, the U-Net architecture is integrated to modify the segmentation branch to improve the segmentation effect. In data engineering, two classification schemes are designed, one according to the function of teeth, the other according to the position of teeth. Results: Based on the data of 400 panoramic X-ray films of teeth, we combined migration learning to conduct experiments on the TensorFlow deep learning framework. The experimental results show that compared with other methods, the classification and segmentation of teeth can be realized simultaneously in this paper, with an accuracy of more than 90%. Compared with the original model, the improved Mask R-CNN proposed in this paper improves the segmentation recall rate by 10%. In the proposed classification scheme, the accuracy of classification based on tooth function is 3% higher than that based on tooth position.Conclusions: The model proposed in this paper combines the two tasks of classification and segmentation, avoids the repetitive training of the model, and improves the segmentation precision with the improved segmentation branch. Compared with the recall rate traditional methods of tooth function classification, the proposed method based on tooth function has better classification effect.

scholarly journals UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rui Miao ◽  
Xin Dong ◽  
Sheng-Li Xie ◽  
Yong Liang ◽  
Sio-Long Lo
Keyword(s):  
Recognition Task ◽  
Training Model ◽  
Learning Model ◽  
Model Parameters ◽  
X Ray ◽  
Learning Framework ◽  
Fast Screening ◽  
Rapid Spread ◽  
Meta Learning ◽  
Multi Class Classification

Abstract Background With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. Methods This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. Results The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3–10% higher than that of the existing models. Conclusion In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

scholarly journals Classification Of X-ray Images For Detecting Covid-19 Using Deep Transfer Learning

2020 ◽  
Author(s):  
Nidhi Bansal ◽  
S.Srid
Keyword(s):  
Transfer Learning ◽  
Network Models ◽  
World Health ◽  
Learning Potential ◽  
Neural Network Models ◽  
X Ray ◽  
Learning Framework ◽  
Health Organization ◽  
High Level

Abstract The coronavirus disease COVID-19 eruption is stated as a pandemic by the World Health Organization. It is affecting around 212 countries and territories across the globe. There is a need to constantly analyze and find patterns from lungs X-Ray images. Early diagnosis can constraint the exposure of person and aids to bound the feast of the virus. The manual diagnosis is quite tedious and time-consuming process. The main aim of this paper is to explore the transfer learning potential. A deep learning framework is proposed adopting the capability of pretrained Deep Convolutional Neural Network models with transfer learning. This assists in classification of the chest X-Ray Images with high level of accuracy. An analysis is done with utilization of six pretrained models – VGG16, VGG19, ResNet50V2, InceptionV3, Xception and NASNetLarge. The experiment results showed that the highest accuracy obtained was 97% using VGG16 and VGG19 with sensitivity and specificity of 100% and 94% respectively.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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