Performance Evaluation of Convolutional Neural Network Using Synthetic Medical Data Augmentation Generated by GAN

2021 ◽  
Author(s):  
Ramesh Adhikari ◽  
Suresh Pokharel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Medical Diagnostics ◽  
Generative Adversarial Networks ◽  
Generalization Capability ◽  
X Ray ◽  
Original Dataset ◽  
Unseen Data

Data augmentation is widely used in image processing and pattern recognition problems in order to increase the richness in diversity of available data. It is commonly used to improve the classification accuracy of images when the available datasets are limited. Deep learning approaches have demonstrated an immense breakthrough in medical diagnostics over the last decade. A significant amount of datasets are needed for the effective training of deep neural networks. The appropriate use of data augmentation techniques prevents the model from over-fitting and thus increases the generalization capability of the network while testing afterward on unseen data. However, it remains a huge challenge to obtain such a large dataset from rare diseases in the medical field. This study presents the synthetic data augmentation technique using Generative Adversarial Networks to evaluate the generalization capability of neural networks using existing data more effectively. In this research, the convolutional neural network (CNN) model is used to classify the X-ray images of the human chest in both normal and pneumonia conditions; then, the synthetic images of the X-ray from the available dataset are generated by using the deep convolutional generative adversarial network (DCGAN) model. Finally, the CNN model is trained again with the original dataset and augmented data generated using the DCGAN model. The classification performance of the CNN model is improved by 3.2% when the augmented data were used along with the originally available dataset.

Using Convolutional Neural Networks for the Detection of Spinal Transverse Processes

2019 ◽  
Author(s):  
Victoria Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transverse Process ◽  
Classification Model ◽  
Ultrasound Images ◽  
Ultrasound Scan ◽  
X Ray ◽  
Scoliosis Screening

Introduction: Scoliosis, an excessive curvature of the spine, affects approximately 1 in 1,000 individuals. As a result, there have formerly been implementations of mandatory scoliosis screening procedures. Screening programs are no longer widely used as the harms often outweigh the benefits; it causes many adolescents to undergo frequent diagnosis X-ray procedure This makes spinal ultrasounds an ideal substitute for scoliosis screening in patients, as it does not expose them to those levels of radiation. Spinal curvatures can be accurately computed from the location of spinal transverse processes, by measuring the vertebral angle from a reference line [1]. However, ultrasound images are less clear than x-ray images, making it difficult to identify the spinal processes. To overcome this, we employ deep learning using a convolutional neural network, which is a powerful tool for computer vision and image classification [2]. Method: A total of 2,752 ultrasound images were recorded from a spine phantom to train a convolutional neural network. Subsequently, we took another recording of 747 images to be used for testing. All the ultrasound images from the scans were then segmented manually, using the 3D Slicer (www.slicer.org) software. Next, the dataset was fed through a convolutional neural network. The network used was a modified version of GoogLeNet (Inception v1), with 2 linearly stacked inception models. This network was chosen because it provided a balance between accurate performance, and time efficient computations. Results: Deep learning classification using the Inception model achieved an accuracy of 84% for the phantom scan.  Conclusion: The classification model performs with considerable accuracy. Better accuracy needs to be achieved, possibly with more available data and improvements in the classification model.  Acknowledgements: G. Fichtinger is supported as a Canada Research Chair in Computer-Integrated Surgery. This work was funded, in part, by NIH/NIBIB and NIH/NIGMS (via grant 1R01EB021396-01A1 - Slicer+PLUS: Point-of-Care Ultrasound) and by CANARIE’s Research Software Program.    Figure 1: Ultrasound scan containing a transverse process (left), and ultrasound scan containing no transverse process (right).                                Figure 2: Accuracy of classification for training (red) and validation (blue). References:           Ungi T, King F, Kempston M, Keri Z, Lasso A, Mousavi P, Rudan J, Borschneck DP, Fichtinger G. Spinal Curvature Measurement by Tracked Ultrasound Snapshots. Ultrasound in Medicine and Biology, 40(2):447-54, Feb 2014.           Krizhevsky A, Sutskeyer I, Hinton GE. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25:1097-1105. 

scholarly journals An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Okeke Stephen ◽  
Mangal Sain ◽  
Uchenna Joseph Maduh ◽  
Do-Un Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Classification Performance ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

scholarly journals Using Convolutional Neural Networks with Multiple Thermal Sensors for Unobtrusive Pose Recognition

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6932
Author(s):  
Matthew Burns ◽  
Federico Cruciani ◽  
Philip Morrow ◽  
Chris Nugent ◽  
Sally McClean
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Daily Living ◽  
Care Home ◽  
Infrared Sensors ◽  
Trade Off ◽  
Pose Recognition ◽  
Original Dataset ◽  
Level Of Understanding

The desire to remain living in one’s own home rather than a care home by those in need of 24/7 care is one that requires a level of understanding for the actions of an environment’s inhabitants. This can potentially be accomplished with the ability to recognise Activities of Daily Living (ADLs); however, this research focuses first on producing an unobtrusive solution for pose recognition where the preservation of privacy is a primary aim. With an accurate manner of predicting an inhabitant’s poses, their interactions with objects within the environment and, therefore, the activities they are performing, can begin to be understood. This research implements a Convolutional Neural Network (CNN), which has been designed with an original architecture derived from the popular AlexNet, to predict poses from thermal imagery that have been captured using thermopile infrared sensors (TISs). Five TISs have been deployed within the smart kitchen in Ulster University where each provides input to a corresponding trained CNN. The approach is evaluated using an original dataset and an F1-score of 0.9920 was achieved with all five TISs. The limitations of utilising a ceiling-based TIS are investigated and each possible permutation of corner-based TISs is evaluated to satisfy a trade-off between the number of TISs, the total sensor cost and the performances. These tests are also promising as F1-scores of 0.9266, 0.9149 and 0.8468 were achieved with the isolated use of four, three, and two corner TISs, respectively.

scholarly journals Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249404
Author(s):  
Jeongtae Son ◽  
Dongsup Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Discovery ◽  
Ligand Binding ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Docking Simulation ◽  
Computational Time ◽  
Binding Affinities

Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding affinity. Graph convolutional neural networks reduce the computational time and resources that are normally required by the traditional convolutional neural network models. In this technique, the structure of a protein-ligand complex is represented as a graph of multiple adjacency matrices whose entries are affected by distances, and a feature matrix that describes the molecular properties of the atoms. We evaluated the predictive power of GraphBAR for protein-ligand binding affinities by using PDBbind datasets and proved the efficiency of the graph convolution. Given the computational efficiency of graph convolutional neural networks, we also performed data augmentation to improve the model performance. We found that data augmentation with docking simulation data could improve the prediction accuracy although the improvement seems not to be significant. The high prediction performance and speed of GraphBAR suggest that such networks can serve as valuable tools in drug discovery.

Identification of Thoracic Diseases by Exploiting Deep Neural Networks (Preprint)

2020 ◽  
Author(s):  
Albahli Saleh ◽  
Ali Alkhalifah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Medical Image Analysis ◽  
Medical Community ◽  
Learning Models ◽  
X Ray ◽  
Chest Disease

BACKGROUND To diagnose cardiothoracic diseases, a chest x-ray (CXR) is examined by a radiologist. As more people get affected, doctors are becoming scarce especially in developing countries. However, with the advent of image processing tools, the task of diagnosing these cardiothoracic diseases has seen great progress. A lot of researchers have put in work to see how the problems associated with medical images can be mitigated by using neural networks. OBJECTIVE Previous works used state-of-the-art techniques and got effective results with one or two cardiothoracic diseases but could lead to misclassification. In our work, we adopted GANs to synthesize the chest radiograph (CXR) to augment the training set on multiple cardiothoracic diseases to efficiently diagnose the chest diseases in different classes as shown in Figure 1. In this regard, our major contributions are classifying various cardiothoracic diseases to detect a specific chest disease based on CXR, use the advantage of GANs to overcome the shortages of small training datasets, address the problem of imbalanced data; and implementing optimal deep neural network architecture with different hyper-parameters to improve the model with the best accuracy. METHODS For this research, we are not building a model from scratch due to computational restraints as they require very high-end computers. Rather, we use a Convolutional Neural Network (CNN) as a class of deep neural networks to propose a generative adversarial network (GAN) -based model to generate synthetic data for training the data as the amount of the data is limited. We will use pre-trained models which are models that were trained on a large benchmark dataset to solve a problem similar to the one we want to solve. For example, the ResNet-152 model we used was initially trained on the ImageNet dataset. RESULTS After successful training and validation of the models we developed, ResNet-152 with image augmentation proved to be the best model for the automatic detection of cardiothoracic disease. However, one of the main problems associated with radiographic deep learning projects and research is the scarcity and unavailability of enough datasets which is a key component of all deep learning models as they require a lot of data for training. This is the reason why some of our models had image augmentation to increase the number of images without duplication. As more data are collected in the field of chest radiology, the models could be retrained to improve the accuracies of the models as deep learning models improve with more data. CONCLUSIONS This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using deep learning models, the research aims to evaluate the effectiveness and accuracy of different convolutional neural network models in the automatic diagnosis of cardiothoracic diseases from x-ray images compared to diagnosis by experts in the medical community.

scholarly journals Selection of neural network architecture and data augmentation procedures for predicting the course of cardiovascular diseases

2021 ◽  
Vol 2094 (3) ◽  
pp. 032037
Author(s):  
M G Dorrer ◽  
S E Golovenkin ◽  
S Yu Nikulina ◽  
Yu V Orlova ◽  
E Yu Pelipeckaya ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cardiovascular Diseases ◽  
Network Architecture ◽  
Data Augmentation ◽  
Case Histories ◽  
Neural Network Architecture ◽  
Original Dataset ◽  
Augmentation Procedure ◽  
Selection Of

Abstract The article solves the problem of creating models for predicting the course and complications of cardiovascular diseases. Artificial neural networks based on the Keras library are used. The original dataset includes 1700 case histories. In addition, the dataset augmentation procedure was used. As a result, the overall accuracy exceeded 84%. Furthermore, optimizing the network architecture and dataset has increased the overall accuracy by 17% and precision by 7%.

scholarly journals Plastic Gasket Defect Detection Based on Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xieyi Chen ◽  
Dongyun Wang ◽  
Jinjun Shao ◽  
Jun Fan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Defect Detection ◽  
Data Augmentation ◽  
Surface Defects ◽  
Visual Detection ◽  
Training Data

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network

2019 ◽  
Vol 28 (1) ◽  
pp. 3-12
Author(s):  
Jarosław Kurek ◽  
Joanna Aleksiejuk-Gawron ◽  
Izabella Antoniuk ◽  
Jarosław Górski ◽  
Albina Jegorowa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Good Condition ◽  
Training Data ◽  
Time Data ◽  
Learning Improvement ◽  
Original Dataset ◽  
Augmentation Techniques

This paper presents an improved method for recognizing the drill state on the basis of hole images drilled in a laminated chipboard, using convolutional neural network (CNN) and data augmentation techniques. Three classes were used to describe the drill state: red -- for drill that is worn out and should be replaced, yellow -- for state in which the system should send a warning to the operator, indicating that this element should be checked manually, and green -- denoting the drill that is still in good condition, which allows for further use in the production process. The presented method combines the advantages of transfer learning and data augmentation methods to improve the accuracy of the received evaluations. In contrast to the classical deep learning methods, transfer learning requires much smaller training data sets to achieve acceptable results. At the same time, data augmentation customized for drill wear recognition makes it possible to expand the original dataset and to improve the overall accuracy. The experiments performed have confirmed the suitability of the presented approach to accurate class recognition in the given problem, even while using a small original dataset.

scholarly journals Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110074
Author(s):  
Jingyao Zhang ◽  
Yuan Rao ◽  
Chao Man ◽  
Zhaohui Jiang ◽  
Shaowen Li
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Sample Size ◽  
Data Augmentation ◽  
Small Sample Size ◽  
Small Sample ◽  
Identification Accuracy ◽  
Generative Adversarial Networks ◽  
Training Samples

Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.

scholarly journals Data Augmentation Using Generative Adversarial Networks (GANs) For GAN-Based Detection Of Pneumonia And COVID-19 In Chest X-Ray Images

2021 ◽  
Author(s):  
Saman Motamed ◽  
Patrik Rogalla ◽  
Farzad Khalvati
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
Generative Models ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
X Rays ◽  
X Ray ◽  
Standard Data ◽  
Adversarial Networks ◽  
Augmentation Techniques

Abstract Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.

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