scholarly journals A new way to constrain the densities of intragroup medium in groups of galaxies with convolutional neural networks

2020 ◽  
Vol 497 (4) ◽  
pp. 5090-5102
Author(s):  
A X Shen ◽  
K Bekki
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Mean Squared Error ◽  
Gas Content ◽  
X Ray ◽  
Groups Of Galaxies ◽  
Squared Error ◽  
Ram Pressure ◽  
Density Map ◽  
2D Images

ABSTRACT Ram pressure (RP) can influence the evolution of cold gas content and star formation rates of galaxies. One of the key parameters for the strength of RP is the density of intragroup medium (ρigm), which is difficult to estimate if the X-ray emission from it is too weak to be observed. We propose a new way to constrain ρigm through an application of convolutional neural networks (CNNs) to simulated gas density and kinematic maps galaxies under strong RP. We train CNNs using 9 × 104 2D images of galaxies under various RP conditions, then validate performance with 104 new test images. This new method can be applied to real observational data from ongoing WALLABY and SKA surveys to quickly obtain estimates of ρigm. Simulated galaxy images have 1.0 kpc resolution, which is consistent with that expected from the future WALLABY survey. The trained CNN models predict the normalized IGM density, $\hat{\rho }_{\rm igm}$ where $0.0 \le \hat{\rho }_{\rm igm, n} \lt 10.0$, accurately with root mean squared error values of 0.72, 0.83, and 0.74 for the density, kinematic, and joined 2D maps, respectively. Trained models are unable to predict the relative velocity of galaxies with respect to the IGM (vrel) precisely, and struggle to generalize for different RP conditions. We apply our CNNs to the observed H i column density map of NGC 1566 in the Dorado group to estimate its IGM density.

scholarly journals Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ivan Lorencin ◽  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Anđela Blagojević ◽  
Tijana Šušteršić ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Picture ◽  
Data Augmentation ◽  
Lower Amount ◽  
Training Procedure ◽  
X Ray ◽  
Severe Clinical Picture ◽  
Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

scholarly journals Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 879
Author(s):  
Clíssia Barboza da Silva ◽  
Alysson Alexander Naves Silva ◽  
Geovanny Barroso ◽  
Pedro Takao Yamamoto ◽  
Valter Arthur ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Models ◽  
Sitophilus Zeamais ◽  
Human Consumption ◽  
Storage Pests ◽  
Maize Weevil ◽  
X Ray ◽  
Precise Control ◽  
X Ray Imaging

The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks

2019 ◽  
Vol 38 (5) ◽  
pp. 1197-1206 ◽  
Author(s):  
Hojjat Salehinejad ◽  
Errol Colak ◽  
Tim Dowdell ◽  
Joseph Barfett ◽  
Shahrokh Valaee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Chest X Ray

scholarly journals Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Symptoms ◽  
Early Stage ◽  
World Health ◽  
Complete Analysis ◽  
Screening Process ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

scholarly journals Multiscale Aggregate Networks with Dense Connections for Crowd Counting

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Pengfei Li ◽  
Min Zhang ◽  
Jian Wan ◽  
Ming Jiang
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Image Features ◽  
Convolutional Network ◽  
Crowd Counting ◽  
Squared Error ◽  
Crowd Density ◽  
Density Maps ◽  
Density Map ◽  
Map Decoder

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.

scholarly journals X-ray image based pneumonia classification using convolutional neural networks

2020 ◽  
Vol 6 (20) ◽  
pp. 54-67
Author(s):  
Sarah Badr AlSumairi ◽  
Mohamed Maher Ben Ismail
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Critical Factor ◽  
Classification Problem ◽  
X Rays ◽  
X Ray ◽  
Erroneous Decision ◽  
Short Period

Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.

USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION

2021 ◽  
Author(s):  
Sandi Baressi Šegota ◽  
◽  
Simon Lysdahlgaard ◽  
Søren Hess ◽  
Ronald Antulov
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Classification Model ◽  
Orientation Sensitivity ◽  
X Ray ◽  
Artificial Neural Network Ann ◽  
Image Orientation ◽  
Single Orientation

The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.

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