Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.011
Adéla Hamplová, David Franc, A. Veselý
This article presents the problem of improving the classifier of handwritten letters from historical alphabets, using letter classification algorithms and transliterating them to Latin. We apply it on Palmyrene alphabet, which is a complex alphabet with letters, some of which are very similar to each other. We created a mobile application for Palmyrene alphabet that is able to transliterate hand-written letters or letters that are given as photograph images. At first, the core of the application was based on MobileNet, but the classification results were not suitable enough. In this article, we suggest an improved, better performing convolutional neural network architecture for hand-written letter classifier used in our mobile application. Our suggested new convolutional neural network architecture shows an improvement in accuracy from 0.6893 to 0.9821 by 142% for hand-written model in comparison with the original MobileNet. Future plans are to improve the photographic model as well.
{"title":"An improved classifier and transliterator of hand-written Palmyrene letters to Latin","authors":"Adéla Hamplová, David Franc, A. Veselý","doi":"10.14311/nnw.2022.32.011","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.011","url":null,"abstract":"This article presents the problem of improving the classifier of handwritten letters from historical alphabets, using letter classification algorithms and transliterating them to Latin. We apply it on Palmyrene alphabet, which is a complex alphabet with letters, some of which are very similar to each other. We created a mobile application for Palmyrene alphabet that is able to transliterate hand-written letters or letters that are given as photograph images. At first, the core of the application was based on MobileNet, but the classification results were not suitable enough. In this article, we suggest an improved, better performing convolutional neural network architecture for hand-written letter classifier used in our mobile application. Our suggested new convolutional neural network architecture shows an improvement in accuracy from 0.6893 to 0.9821 by 142% for hand-written model in comparison with the original MobileNet. Future plans are to improve the photographic model as well.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.016
Jun Cui, Lei Su, Ran Wei, Guangxu Li, Hongwei Hu, Xin Dang
As a novel biometric characteristic, the electroencephalogram (EEG) is used for biometric authentication. To solve the challenge of efficiently growing the number of classifications in traditional classification networks and to increase the practicality of engineering, this paper proposes an authentication approach for EEG data based on an attention mechanism and a triplet loss function. The method begins by feeding EEG signals into a deep convolutional network, maps them to 512-dimensional Euclidean space using a long short-term memory network combined with an attention mechanism, and obtains feature vectors for EEG signals with identity information; it then adjusts the network parameters using a triplet loss function, such that the Euclidean distance between feature vectors of similar signals decreases while the distance between signals of a different type increases. Finally, the recognition method is evaluated using publicly available EEG data sets. The experimental results suggest that the method maintains the recognition rate while effectively expanding the classifications of the model, hence thus boosting the practicability of EEG authentication.
{"title":"EEG authentication based on deep learning of triplet loss","authors":"Jun Cui, Lei Su, Ran Wei, Guangxu Li, Hongwei Hu, Xin Dang","doi":"10.14311/nnw.2022.32.016","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.016","url":null,"abstract":"As a novel biometric characteristic, the electroencephalogram (EEG) is used for biometric authentication. To solve the challenge of efficiently growing the number of classifications in traditional classification networks and to increase the practicality of engineering, this paper proposes an authentication approach for EEG data based on an attention mechanism and a triplet loss function. The method begins by feeding EEG signals into a deep convolutional network, maps them to 512-dimensional Euclidean space using a long short-term memory network combined with an attention mechanism, and obtains feature vectors for EEG signals with identity information; it then adjusts the network parameters using a triplet loss function, such that the Euclidean distance between feature vectors of similar signals decreases while the distance between signals of a different type increases. Finally, the recognition method is evaluated using publicly available EEG data sets. The experimental results suggest that the method maintains the recognition rate while effectively expanding the classifications of the model, hence thus boosting the practicability of EEG authentication.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.002
S. Jozová, Evženie Uglickich, I. Nagy, R. Likhonina
The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.
{"title":"Modeling of discrete questionnaire data with dimension reduction","authors":"S. Jozová, Evženie Uglickich, I. Nagy, R. Likhonina","doi":"10.14311/nnw.2022.32.002","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.002","url":null,"abstract":"The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.004
Fengjuan Qiao, Bin Li, Mengqi Gao, Jiang Li
The intelligent diagnosis of cardiovascular diseases is a topic of great interest. Many electrocardiogram (ECG) recognition technologies have emerged, but most of them have low recognition accuracy and poor clinical application. To improve the accuracy of ECG classification, this paper proposes a multi-channel neural network framework. Concretely, a multi-channel feature extractor is constructed by using four types of filters, which are weighted according to their importance, as measured by kurtosis. A bidirectional long short-term memory (BLSTM) network structure based on attention mechanism is constructed, and the extracted features are taken as the input of the network, and the algorithm is optimized by attention mechanism. An experiment conducted on the MIT-BIH arrhythmia database shows that the proposed algorithm obtains excellent results, with 99.20 % specificity, 99.87 % sensitivity, and 99.89 % accuracy. Therefore, the algorithm is practical and effective in the clinical diagnosis of cardiovascular diseases.
{"title":"ECG signal classification based on adaptive multi-channel weighted neural network","authors":"Fengjuan Qiao, Bin Li, Mengqi Gao, Jiang Li","doi":"10.14311/nnw.2022.32.004","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.004","url":null,"abstract":"The intelligent diagnosis of cardiovascular diseases is a topic of great interest. Many electrocardiogram (ECG) recognition technologies have emerged, but most of them have low recognition accuracy and poor clinical application. To improve the accuracy of ECG classification, this paper proposes a multi-channel neural network framework. Concretely, a multi-channel feature extractor is constructed by using four types of filters, which are weighted according to their importance, as measured by kurtosis. A bidirectional long short-term memory (BLSTM) network structure based on attention mechanism is constructed, and the extracted features are taken as the input of the network, and the algorithm is optimized by attention mechanism. An experiment conducted on the MIT-BIH arrhythmia database shows that the proposed algorithm obtains excellent results, with 99.20 % specificity, 99.87 % sensitivity, and 99.89 % accuracy. Therefore, the algorithm is practical and effective in the clinical diagnosis of cardiovascular diseases.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67124933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.007
Dongsheng Qing, Jianjun Li, Qiaoling Deng, Shuai Liu
In order to fully understand the objective law of height and DBH growth of loblolly pine trees and exploring the best DBH (Diameter at Breast Height) Range for loblolly pine tree height growth, 13 340 loblolly pines with initial DBH between 1 inch and 7 inch were selected from Alabama as research objects, and statistics on its growth from 2000 to 2015. Because particle swarm optimization (PSO) is suitable for solving non-linear problems, the optimal DBH of loblolly pine is transformed into the optimization problem of PSO, which quantifies the optimal DBH range of loblolly pine at different scales by mapping strategy. The experimental results show that the range of the breast diameter suitable for the high growth of the pine tree is concentrated between 3.7 inch and 7.3 inch. The height of the pine tree begins to enter a period of rapid growth from a breast diameter of 3.9 inch (ą0.2 inch ). The tree height growth rate reached a maximum at a breast diameter of 6.4 inch (ą0.6 inch ), and the tree height entered a slow growth period after the breast diameter of 11.92 inch (ą0.3 inch). In general, when the breast diameter exceeds 15.26 inch (ą0.3 inch), the height of the pine tree stops growing.
{"title":"Mining and quantifying the optimal DBH range of loblolly pine with improved particle algorithm","authors":"Dongsheng Qing, Jianjun Li, Qiaoling Deng, Shuai Liu","doi":"10.14311/nnw.2022.32.007","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.007","url":null,"abstract":"In order to fully understand the objective law of height and DBH growth of loblolly pine trees and exploring the best DBH (Diameter at Breast Height) Range for loblolly pine tree height growth, 13 340 loblolly pines with initial DBH between 1 inch and 7 inch were selected from Alabama as research objects, and statistics on its growth from 2000 to 2015. Because particle swarm optimization (PSO) is suitable for solving non-linear problems, the optimal DBH of loblolly pine is transformed into the optimization problem of PSO, which quantifies the optimal DBH range of loblolly pine at different scales by mapping strategy. The experimental results show that the range of the breast diameter suitable for the high growth of the pine tree is concentrated between 3.7 inch and 7.3 inch. The height of the pine tree begins to enter a period of rapid growth from a breast diameter of 3.9 inch (ą0.2 inch ). The tree height growth rate reached a maximum at a breast diameter of 6.4 inch (ą0.6 inch ), and the tree height entered a slow growth period after the breast diameter of 11.92 inch (ą0.3 inch). In general, when the breast diameter exceeds 15.26 inch (ą0.3 inch), the height of the pine tree stops growing.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.010
Yuxin Ye, Song Jiang, Shi Wang, Huiying Li
Distant supervision for relation extraction, an effective method to reduce labor costs, has been widely used to search for novel relational facts from text. However, distant supervision always suffers from incorrect labelling problems. Meanwhile, existing methods for noise reduction oftentimes ignore the commonalities in the instances. To alleviate this issue, we propose a distant supervision relation extraction model based on mutual information and multi-level attention. In our proposed method, we calculate mutual information based on the attention mechanism. Mutual information are used to build attention at both word and sentence levels, which is expected to dynamically reduce the influence of noisy instances. Extensive experiments using a benchmark dataset have validated the effectiveness of our proposed method.
{"title":"Distant supervision relation extraction based on mutual information and multi-level attention","authors":"Yuxin Ye, Song Jiang, Shi Wang, Huiying Li","doi":"10.14311/nnw.2022.32.010","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.010","url":null,"abstract":"Distant supervision for relation extraction, an effective method to reduce labor costs, has been widely used to search for novel relational facts from text. However, distant supervision always suffers from incorrect labelling problems. Meanwhile, existing methods for noise reduction oftentimes ignore the commonalities in the instances. To alleviate this issue, we propose a distant supervision relation extraction model based on mutual information and multi-level attention. In our proposed method, we calculate mutual information based on the attention mechanism. Mutual information are used to build attention at both word and sentence levels, which is expected to dynamically reduce the influence of noisy instances. Extensive experiments using a benchmark dataset have validated the effectiveness of our proposed method.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.009
Babak Masoudi, Sebelan Danishvar
Schizophrenia is a complex mental disorder associated with a change in the functional and structural of the brain. Accurate automatic diagnosis of schizophrenia is crucial and still a challenge. In this paper, we propose an automatic diagnosis of schizophrenia disorder method based on the fusion of different neuroimaging features and a deep learning architecture. We propose a deep-multimodal fusion (DMMF) architecture based on gated recurrent unit (GRU) network and 2D-3D convolutional neural networks (CNN). The DMMF model combines functional connectivity (FC) measures extracted from functional magnetic resonance imaging (fMRI) data and low-level features obtained from fMRI, magnetic resonance imaging (MRI), or diffusion tensor imaging (DTI) data and creates latent and discriminative feature maps for classification. The fusion of ROI-based FC with fractional anisotropy (FA) derived from DTI images achieved state-of-theart diagnosis-accuracy of 99.50% and an area under the curve (AUC) of 99.7% on COBRE dataset. The results are promising for the combination of features. The high accuracy and AUC in our experiments show that the proposed deep learning architecture can extract latent patterns from neuroimaging data and can help to achieve accurate classification of schizophrenia and healthy groups.
{"title":"Deep multi-modal schizophrenia disorder diagnosis via a GRU-CNN architecture","authors":"Babak Masoudi, Sebelan Danishvar","doi":"10.14311/nnw.2022.32.009","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.009","url":null,"abstract":"Schizophrenia is a complex mental disorder associated with a change in the functional and structural of the brain. Accurate automatic diagnosis of schizophrenia is crucial and still a challenge. In this paper, we propose an automatic diagnosis of schizophrenia disorder method based on the fusion of different neuroimaging features and a deep learning architecture. We propose a deep-multimodal fusion (DMMF) architecture based on gated recurrent unit (GRU) network and 2D-3D convolutional neural networks (CNN). The DMMF model combines functional connectivity (FC) measures extracted from functional magnetic resonance imaging (fMRI) data and low-level features obtained from fMRI, magnetic resonance imaging (MRI), or diffusion tensor imaging (DTI) data and creates latent and discriminative feature maps for classification. The fusion of ROI-based FC with fractional anisotropy (FA) derived from DTI images achieved state-of-theart diagnosis-accuracy of 99.50% and an area under the curve (AUC) of 99.7% on COBRE dataset. The results are promising for the combination of features. The high accuracy and AUC in our experiments show that the proposed deep learning architecture can extract latent patterns from neuroimaging data and can help to achieve accurate classification of schizophrenia and healthy groups.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.006
Keke Ji, Zhengzhong Li, Jian Chen, Guanyan Wang, Keliang Liu, Yi Luo
Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.
{"title":"Freeway accident duration prediction based on social network information","authors":"Keke Ji, Zhengzhong Li, Jian Chen, Guanyan Wang, Keliang Liu, Yi Luo","doi":"10.14311/nnw.2022.32.006","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.006","url":null,"abstract":"Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.018
Shahid Khan, Altaf Mukati, Syed Sajjad Hussain Rizvi, N. Yazdanie
Segmentation of an individual tooth in dental radiographs has great significance in the process of orthodontics surgeries and dentistry. Machine learning techniques, especially deep convolutional neural networks can play a key role in revolutionizing the way orthodontics surgeons and dentists work. Lately, many researchers have been working on tooth segmentation in 3D volumetric dental scans with a great degree of success, but to the best of our knowledge, there is no pretrained neural network available publicly for performing tooth segmentation in 3D cone-beam dental CT scans. The methods which so far have been proposed by the researchers in this domain are based on complex multistep pipelines. This lack of the availability of a pre-trained model blocks the path for further explorations in this domain. In this research, we have produced a deep learning model for tooth segmentation from CBCT dental radiographs. The proposed model can segment teeth in CBCT scans in a single step. To train the proposed model, we obtained a dataset consisting of 70 3D CBCT volumes from a local health facility. We labeled the ground truth through a semi-automatic method and trained our neural network. The training yielded a validation accuracy of 95.57% on a binary class semantic segmentation of the 3D CBCT volumes. The model is successfully able to segment teeth, regardless of their type from the background in a single step. This eliminates the need of having a complex and lengthy pipeline which many researchers have been proposing. The proposed model can be extended by incorporating labeling schemes. The custom labeling schemes will help healthcare professionals to perform the labeling as per their needs. The produced model can also provide a basis for further research in this domain.
{"title":"Tooth segmentation in 3D cone-beam CT images using deep convolutional neural network","authors":"Shahid Khan, Altaf Mukati, Syed Sajjad Hussain Rizvi, N. Yazdanie","doi":"10.14311/nnw.2022.32.018","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.018","url":null,"abstract":"Segmentation of an individual tooth in dental radiographs has great significance in the process of orthodontics surgeries and dentistry. Machine learning techniques, especially deep convolutional neural networks can play a key role in revolutionizing the way orthodontics surgeons and dentists work. Lately, many researchers have been working on tooth segmentation in 3D volumetric dental scans with a great degree of success, but to the best of our knowledge, there is no pretrained neural network available publicly for performing tooth segmentation in 3D cone-beam dental CT scans. The methods which so far have been proposed by the researchers in this domain are based on complex multistep pipelines. This lack of the availability of a pre-trained model blocks the path for further explorations in this domain. In this research, we have produced a deep learning model for tooth segmentation from CBCT dental radiographs. The proposed model can segment teeth in CBCT scans in a single step. To train the proposed model, we obtained a dataset consisting of 70 3D CBCT volumes from a local health facility. We labeled the ground truth through a semi-automatic method and trained our neural network. The training yielded a validation accuracy of 95.57% on a binary class semantic segmentation of the 3D CBCT volumes. The model is successfully able to segment teeth, regardless of their type from the background in a single step. This eliminates the need of having a complex and lengthy pipeline which many researchers have been proposing. The proposed model can be extended by incorporating labeling schemes. The custom labeling schemes will help healthcare professionals to perform the labeling as per their needs. The produced model can also provide a basis for further research in this domain.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.14311/nnw.2022.32.013
Jan Merta, T. Brandejsky
This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.
{"title":"Two-layer genetic programming","authors":"Jan Merta, T. Brandejsky","doi":"10.14311/nnw.2022.32.013","DOIUrl":"https://doi.org/10.14311/nnw.2022.32.013","url":null,"abstract":"This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"256 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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