scholarly journals Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine

2021 ◽  
Author(s):  
Mizuho Mori ◽  
Yoshiko Ariji ◽  
Motoki Fukuda ◽  
Tomoya Kitano ◽  
Takuma Funakoshi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Learning Systems ◽  
Learning Technology ◽  
System 2 ◽  
Potential Benefits ◽  
System 1 ◽  
Periapical Radiography

Abstract Objectives The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques. Methods We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively. The learning model of system 1 was created with only the classification process, whereas system 2 consisted of both the segmentation and classification models. In each model, 500 epochs of training were performed using AlexNet and U-net for classification and segmentation, respectively. The segmentation results were evaluated by the intersection over union method, with values of 0.6 or more considered as success. The classification results were compared between the two systems. Results The segmentation performance of system 2 was recall, precision, and F measure of 0.937, 0.961, and 0.949, respectively. System 2 showed better classification performance values than those obtained by system 1. The area under the receiver operating characteristic curve values differed significantly between system 1 (0.649) and system 2 (0.927). Conclusions The deep learning systems we created appeared to have potential benefits in evaluation of the technical positioning quality of periapical radiographs through the use of segmentation and classification functions.

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

scholarly journals Comparison of Attention-based Deep LearningModels for EEG Classification

2021 ◽  
Author(s):  
Giulia Cisotto ◽  
Alessio Zanga ◽  
Joanna Chlebus ◽  
Italo Zoppis ◽  
Sara Manzoni ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Classification Performance ◽  
Classification Problems ◽  
Large Variability ◽  
Eeg Classification ◽  
Time Frequency ◽  
Promising Strategy ◽  
Complex Reasoning

Abstract Deep Learning (DL) has recently shown promising classification performance in Electroencephalography (EEG) in many different scenarios. However, the complex reasoning of such models often prevent the user to explain their classification abilities. Attention, one of the most recent and influential ideas in DL, allows the models to learn which portions of the data are relevant to the final classification output. In this work, we compared three attention-enhanced DL models, the brand-new InstaGATs , an LSTM with attention and a CNN with attention. We used these models to classify normal and abnormal, including artifactual and pathological, EEG patterns in three different datasets. We achieved the state of the art in all classification problems, regardless the large variability of the datasets and the simple architecture of the attention-enhanced models. Additionally, we proved that, depending on how the attention mechanism is applied and where the attention layer is located in the model, we can alternatively leverage the information contained in the time, frequency or space domain of the EEG dataset. Therefore, attention represents a promising strategy to evaluate the quality of the EEG information, and its relevance for classification, in different real-world scenarios.

scholarly journals Exploring the Role of Deep Learning Technology in the Sustainable Development of the Music Production Industry

2020 ◽  
Vol 12 (2) ◽  
pp. 625
Author(s):  
Sung-Shun Weng ◽  
Hung-Chia Chen
Keyword(s):  
Sustainable Development ◽  
Deep Learning ◽  
Learning Technology ◽  
Pls Regression ◽  
The Sustainable Development ◽  
Music Production ◽  
Human Creativity ◽  
Production Industry

This study explores the role of deep learning technology in the sustainable development of the music production industry. This article surveys the opinions of Taiwanese music creation professionals and uses partial least squares (PLS) regression to analyze and elucidate the importance of deep learning technology in the music production industry. We found that deep learning cannot replace human creativity, but greater investment in this technology can improve the quality of music creation. In order to achieve sustainable development in the music production industry, industry participants need to awaken consumers’ awareness of music quality, actively enhance the unique value of their art, and strengthen cooperation between industries to provide a friendly environment for listeners.

Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle

2021 ◽  
pp. 20200611
Author(s):  
Masako Nishiyama ◽  
Kenichiro Ishibashi ◽  
Yoshiko Ariji ◽  
Motoki Fukuda ◽  
Wataru Nishiyama ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Characteristic Curve ◽  
Mandibular Condyle ◽  
Internal Validity ◽  
Classification Performance ◽  
Data Sets ◽  
Panoramic Radiographs ◽  
Significant Difference ◽  
Low Performance

Objective: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities. Methods: Panoramic radiographs of 100 condyles with and without fractures were collected from two hospitals and a fivefold cross-validation method was employed to construct and evaluate the DL models. The internal and external validities of classification performance were evaluated as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: For internal validity, high classification performance was obtained, with AUC values of >0.85. Conversely, external validity for the data sets from the two hospitals exhibited low performance. Using combined data sets from both hospitals, the DL model exhibited high performance, which was slightly superior or equal to that of the internal validity but without a statistically significant difference. Conclusion: The constructed DL model can be clinically employed for diagnosing fractures of the mandibular condyle using panoramic radiographs. However, the domain shift phenomenon should be considered when generalizing DL systems.

scholarly journals An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chi-Long Chen ◽  
Chi-Chung Chen ◽  
Wei-Hsiang Yu ◽  
Szu-Hua Chen ◽  
Yu-Chan Chang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Digital Pathology ◽  
Classification Performance ◽  
Multiple Instance Learning ◽  
Data Set ◽  
Memory Mechanism ◽  
Strong Localization ◽  
Training Approach

AbstractDeep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping.

scholarly journals E-learning Systems in the Russian Mining and Metallurgical Sector

2021 ◽  
Vol 2021 (3) ◽  
pp. 315-325
Author(s):  
M. Kazakova ◽  
Tat'yana Selivanova
Keyword(s):  
Digital Economy ◽  
Learning Systems ◽  
Learning System ◽  
Learning Technology ◽  
Employee Survey ◽  
Electronic Learning ◽  
Open Questions ◽  
Educational Processes ◽  
E Learning

This article focuses on the implementation of the national programme "Digital Economy of the Russian Federation" and the federal project "Human Resources for the Digital Economy" in the organisation of the mining and metallurgical complex. The object of the study is e-learning systems. The subject of the study is the processes of using e-learning systems in the organization-basis of the study relating to the mining and metallurgical complex in Russia. The aim of the work is to develop recommendations for the management of organizations that are going to implement distance learning technology in the educational processes of staff. The methodology of the study includes a survey of employees of the organisation under study on the attitude and quality of training in an e-learning system. The experience of personnel training in enterprises with the help of different information systems is considered. The focus is on the analysis of the use of the training and control system "Olympox" in the organisation of the mining and metallurgy industry. The results of an employee survey are given to find out their attitude to the introduction of an electronic learning information system and to the organization of training processes in it. Recommendations on the improvement of information training systems used for personnel assessment and development are offered. Based on the results of the study, it was determined that the quality of personnel development depends on the competent combination of training in a face-to-face format with technology that allows you to practice theory in the form of tests and open questions and practice in the form of cases and exercises to practice working situations in conditions as close to the real.

Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

2020 ◽  
Vol 77 (9) ◽  
pp. 597-602
Author(s):  
Xiaohua Wang ◽  
Juezhao Yu ◽  
Qiao Zhu ◽  
Shuqiang Li ◽  
Zanmei Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
X Ray ◽  
Testing Dataset ◽  
Learning Techniques ◽  
History Of ◽  
Digital Chest ◽  
Chest X Ray

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

A deep learning-based quality assessment model of collaboratively edited documents: A case study of Wikipedia

2019 ◽  
pp. 016555151987764
Author(s):  
Ping Wang ◽  
Xiaodan Li ◽  
Renli Wu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Complete Information ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Learning Models ◽  
Proposed Model

Wikipedia is becoming increasingly critical in helping people obtain information and knowledge. Its leading advantage is that users can not only access information but also modify it. However, this presents a challenging issue: how can we measure the quality of a Wikipedia article? The existing approaches assess Wikipedia quality by statistical models or traditional machine learning algorithms. However, their performance is not satisfactory. Moreover, most existing models fail to extract complete information from articles, which degrades the model’s performance. In this article, we first survey related works and summarise a comprehensive feature framework. Then, state-of-the-art deep learning models are introduced and applied to assess Wikipedia quality. Finally, a comparison among deep learning models and traditional machine learning models is conducted to validate the effectiveness of the proposed model. The models are compared extensively in terms of their training and classification performance. Moreover, the importance of each feature and the importance of different feature sets are analysed separately.

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