Training dataset generation for automatic registration of a duplicate bridge game

Vision System ◽

Automatic Generation ◽

Detection Task ◽

Training Dataset ◽

Automatic Registration ◽

Playing Card

This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection. The solution allows to skip the time-consuming processes of manual image collecting and labelling recognised objects. The YOLOv4 network trained on the generated dataset achieved an efficiency of 99.8% in the cards detection task. The proposed method is a part of a project that aims to automate the process of broadcasting duplicate bridge competitions using a vision system and neural networks.

Training dataset generation for automatic registration of a duplicate bridge game

10.36227/techrxiv.16676734.v1 ◽

2021 ◽

Author(s):

Piotr Wzorek ◽

Tomasz Kryjak

Keyword(s):

Neural Network ◽

Neural Networks ◽

Vision System ◽

Automatic Generation ◽

Detection Task ◽

Training Dataset ◽

Automatic Registration ◽

Playing Card

Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study

BMC Medical Imaging ◽

10.1186/s12880-021-00710-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pei Yang ◽

Yong Pi ◽

Tao He ◽

Jiangming Sun ◽

Jianan Wei ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Predictive Value ◽

Automatic Differentiation ◽

External Validation ◽

Image Feature ◽

Training Dataset ◽

Internal Validation

Abstract Background 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. Methods We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model’s performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. Results The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for ‘diffusely increased,’ 0.997 for ‘diffusely decreased,’ 0.998 for ‘focal increased,’ and 0.945 for ‘heterogeneous uptake’ in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. Conclusions Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT

Radiation Oncology ◽

10.1186/s13014-021-01867-6 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Hideaki Hirashima ◽

Mitsuhiro Nakamura ◽

Pascal Baillehache ◽

Yusuke Fujimoto ◽

Shota Nakagawa ◽

...

Keyword(s):

Neural Network ◽

Volumetric Modulated Arc Therapy ◽

Intensity Modulated Radiotherapy ◽

Training Dataset ◽

Dice Similarity Coefficient ◽

Optimal Model ◽

Pelvic Region ◽

Segmentation Accuracy

Abstract Background This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region. Methods A total of 470 prostate cancer patients who had undergone intensity-modulated radiotherapy or volumetric-modulated arc therapy were enrolled. Our model was based on FusionNet, a fully residual deep CNN developed to semantically segment biological images. To develop the CNN-based segmentation software, 450 patients were randomly selected and separated into the training, validation and testing groups (270, 90, and 90 patients, respectively). In Experiment 1, to determine the optimal model, we first assessed the segmentation accuracy according to the size of the training dataset (90, 180, and 270 patients). In Experiment 2, the effect of varying the number of training labels on segmentation accuracy was evaluated. After determining the optimal model, in Experiment 3, the developed software was used on the remaining 20 datasets to assess the segmentation accuracy. The volumetric dice similarity coefficient (DSC) and the 95th-percentile Hausdorff distance (95%HD) were calculated to evaluate the segmentation accuracy for each organ in Experiment 3. Results In Experiment 1, the median DSC for the prostate were 0.61 for dataset 1 (90 patients), 0.86 for dataset 2 (180 patients), and 0.86 for dataset 3 (270 patients), respectively. The median DSCs for all the organs increased significantly when the number of training cases increased from 90 to 180 but did not improve upon further increase from 180 to 270. The number of labels applied during training had a little effect on the DSCs in Experiment 2. The optimal model was built by 270 patients and four organs. In Experiment 3, the median of the DSC and the 95%HD values were 0.82 and 3.23 mm for prostate; 0.71 and 3.82 mm for seminal vesicles; 0.89 and 2.65 mm for the rectum; 0.95 and 4.18 mm for the bladder, respectively. Conclusions We have developed a CNN-based segmentation software for the male pelvic region and demonstrated that the CNN-based segmentation software is efficient for the male pelvic region.

Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

Applied Sciences ◽

10.3390/app10062104 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2104

Author(s):

Michał Tomaszewski ◽

Paweł Michalski ◽

Jakub Osuchowski

Keyword(s):

Neural Network ◽

Neural Networks ◽

Object Detection ◽

Deep Neural Networks ◽

Detection Efficiency ◽

Training Data ◽

Training Dataset ◽

Training Set ◽

Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments - Advances in Computational Intelligence and Robotics ◽

Deep Convolutional Neural Network for Object Classification

10.4018/978-1-7998-6690-9.ch016 ◽

2021 ◽

pp. 317-343

Author(s):

Amira Ahmad Al-Sharkawy ◽

Gehan A. Bahgat ◽

Elsayed E. Hemayed ◽

Samia Abdel-Razik Mashali

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computational Models ◽

Human Performance ◽

Deep Neural Networks ◽

Object Classification ◽

Classification Problem ◽

Object Appearance

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.

Research and Application of Ancient Chinese Pattern Restoration Based on Deep Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/2691346 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xuhui Fu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Image Restoration ◽

Deep Convolutional Neural Networks ◽

Multilayer Network ◽

Restoration Method ◽

Restoration Effect

In recent years, deep learning, as a very popular artificial intelligence method, can be said to be a small area in the field of image recognition. It is a type of machine learning, actually derived from artificial neural networks, and is a method used to learn the characteristics of sample data. It is a multilayer network, which can learn the information from the bottom to the top of the image through the multilayer network, so as to extract the characteristics of the sample, and then perform identification and classification. The purpose of deep learning is to make the machine have the same analytical and learning capabilities as the human brain. The ability of deep learning in data processing (including images) is unmatched by other methods, and its achievements in recent years have left other methods behind. This article comprehensively reviews the application research progress of deep convolutional neural networks in ancient Chinese pattern restoration and mainly focuses on the research based on deep convolutional neural networks. The main tasks are as follows: (1) a detailed and comprehensive introduction to the basic knowledge of deep convolutional neural and a summary of related algorithms along the three directions of text preprocessing, learning, and neural networks are provided. This article focuses on the related mechanism of traditional pattern repair based on deep convolutional neural network and analyzes the key structure and principle. (2) Research on image restoration models based on deep convolutional networks and adversarial neural networks is carried out. The model is mainly composed of four parts, namely, information masking, feature extraction, generating network, and discriminant network. The main functions of each part are independent and interdependent. (3) The method based on the deep convolutional neural network and the other two methods are tested on the same part of the Qinghai traditional embroidery image data set. From the final evaluation index of the experiment, the method in this paper has better evaluation index than the traditional image restoration method based on samples and the image restoration method based on deep learning. In addition, from the actual image restoration effect, the method in this paper has a better image restoration effect than the other two methods, and the restoration results produced are more in line with the habit of human observation with the naked eye.

Visual Analytics for Network Events Classification in LAN With Deep Convolutional Neural Network

10.21203/rs.3.rs-34589/v1 ◽

2020 ◽

Author(s):

Yuwei Sun ◽

Hideya Ochiai ◽

Hiroshi Esaki

Keyword(s):

Neural Network ◽

Network Traffic ◽

Visual Analytics ◽

Training Dataset ◽

Validation Dataset ◽

Hilbert Curve ◽

Feature Maps ◽

F Measure

Abstract This article illustrates a method of visualizing network traffic in LAN based on the Hilbert Curve structure and the array exchange and projection, with nine types of protocols’ communication frequency information as the discriminators, the results of which we call them feature maps of network events. Several known scan cases are simulated in LANs and network traffic is collected for generating feature maps under each case. In order to solve this multi-label classification task, we adopt and train a deep convolutional neural network (DCNN), in two different network environments with feature maps as the input data, and different scan cases as the labels. We separate datasets with a ratio of 4:1 into the training dataset and the validation dataset. Then, based on the micro scores and the macro scores of the validation, we evaluate performance of the scheme, achieving macro-F-measure scores of 0.982 and 0.975, and micro-F-measure scores of 0.976 and 0.965 separately in these two LANs.

Deriving Metamodels to Relate Machine Learning Quality to Design Repository Characteristics in the Context of Additive Manufacturing

Volume 11A: 46th Design Automation Conference (DAC) ◽

10.1115/detc2020-22518 ◽

2020 ◽

Author(s):

Glen Williams ◽

Nicholas A. Meisel ◽

Timothy W. Simpson ◽

Christopher McComb

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Additive Manufacturing ◽

Convolutional Neural Networks ◽

Spatial Diversity ◽

Computational Effort ◽

Training Dataset ◽

The Creation

Abstract The widespread growth of additive manufacturing, a field with a complex informatic “digital thread”, has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components’ rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related to AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the sub-repositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate. Moreover, the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutional neural network accuracy.

Building an Otoscopic screening prototype tool using deep learning

Journal of Otolaryngology - Head and Neck Surgery ◽

10.1186/s40463-019-0389-9 ◽

2019 ◽

Vol 48 (1) ◽

Cited By ~ 5

Author(s):

Devon Livingstone ◽

Aron S. Talai ◽

Justin Chau ◽

Nils D. Forkert

Keyword(s):

Neural Network ◽

Deep Learning ◽

Primary Care Providers ◽

Training Dataset ◽

Great Success ◽

Care Providers ◽

Tympanostomy Tubes ◽

Tympanic Membranes

Abstract Background Otologic diseases are often difficult to diagnose accurately for primary care providers. Deep learning methods have been applied with great success in many areas of medicine, often outperforming well trained human observers. The aim of this work was to develop and evaluate an automatic software prototype to identify otologic abnormalities using a deep convolutional neural network. Material and methods A database of 734 unique otoscopic images of various ear pathologies, including 63 cerumen impactions, 120 tympanostomy tubes, and 346 normal tympanic membranes were acquired. 80% of the images were used for the training of a convolutional neural network and the remaining 20% were used for algorithm validation. Image augmentation was employed on the training dataset to increase the number of training images. The general network architecture consisted of three convolutional layers plus batch normalization and dropout layers to avoid over fitting. Results The validation based on 45 datasets not used for model training revealed that the proposed deep convolutional neural network is capable of identifying and differentiating between normal tympanic membranes, tympanostomy tubes, and cerumen impactions with an overall accuracy of 84.4%. Conclusion Our study shows that deep convolutional neural networks hold immense potential as a diagnostic adjunct for otologic disease management.

Ukrainian dactyl alphabet gesture recognition using cross platform software and convolutional neural networks

Artificial Intelligence ◽

10.15407/jai2019.03-04.107 ◽

2019 ◽

Vol 24 (3-4) ◽

pp. 107-113

Author(s):

Kondratiuk S.S. ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Sign Language ◽

Convolutional Neural Networks ◽

Gesture Recognition ◽

Training Dataset ◽

Hand Model ◽

Cross Platform ◽

The Cross

The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units of sign language, animation between states of gesture units with a combination of gestures (words). Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input using trained on collected training dataset set convolutional neural network. With the cross platform means technology achieves the ability to run on multiple platforms without re-implementing for each platform