scholarly journals A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Geng Hong ◽  
Xiaoyan Chen ◽  
Jianyong Chen ◽  
Miao Zhang ◽  
Yumeng Ren ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ct Images ◽  
Attention Mechanism ◽  
X Ray ◽  
Multi Scale ◽  
Specificity And Sensitivity ◽  
The World ◽  
Tenfold Cross Validation ◽  
Model Size ◽  
Feature Information

AbstractCoronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale gated multi-head attention depthwise separable CNN (MGMADS-CNN) is proposed, which is based on attention mechanism and depthwise separable convolution. A multi-scale gated multi-head attention mechanism is designed to extract effective feature information from the COVID-19 X-ray and CT images for classification. Moreover, the depthwise separable convolution layers are adopted as MGMADS-CNN’s backbone to reduce the model size and parameters. The LeNet-5, AlexNet, GoogLeNet, ResNet, VGGNet-16, and three MGMADS-CNN models are trained, validated and tested with tenfold cross-validation on X-ray and CT images. The results show that MGMADS-CNN with three attention layers (MGMADS-3) has achieved accuracy of 96.75% on X-ray images and 98.25% on CT images. The specificity and sensitivity are 98.06% and 96.6% on X-ray images, and 98.17% and 98.05% on CT images. The size of MGMADS-3 model is only 43.6 M bytes. In addition, the detection speed of MGMADS-3 on X-ray images and CT images are 6.09 ms and 4.23 ms for per image, respectively. It is proved that the MGMADS-3 can detect and classify COVID-19 faster with higher accuracy and efficiency.

Formation Factor for Heterogeneous Carbonate Rocks Using Multi-scale X-ray CT images

2012 ◽  
Author(s):  
Yildiray Cinar ◽  
Christoph Arns ◽  
Ahmad Dehghan Khalili ◽  
Sefer Yanici
Keyword(s):  
Carbonate Rocks ◽  
Ct Images ◽  
Formation Factor ◽  
X Ray ◽  
Multi Scale

scholarly journals CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Md. Saikat Islam Khan ◽  
Anichur Rahman ◽  
Md. Razaul Karim ◽  
Nasima Islam Bithi ◽  
Shahab Band ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Feature Fusion ◽  
State Of The Art ◽  
Research Community ◽  
Diagnostic Model ◽  
X Ray ◽  
Multi Scale ◽  
The World ◽  
Chest X Ray ◽  
Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

scholarly journals U-Net combined with multi-scale attention mechanism for liver segmentation in CT images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiawei Wu ◽  
Shengqiang Zhou ◽  
Songlin Zuo ◽  
Yiyin Chen ◽  
Weiqin Sun ◽  
...  
Keyword(s):  
Work Experience ◽  
Spatial Location ◽  
Ct Images ◽  
Attention Mechanism ◽  
Dice Similarity Coefficient ◽  
Liver Segmentation ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Spatial Location Information ◽  
Liver Region

Abstract Background The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).

scholarly journals A Residual Attention and Local Context-Aware Network for Road Extraction from High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (24) ◽  
pp. 4958
Author(s):  
Ziwei Liu ◽  
Mingchang Wang ◽  
Fengyan Wang ◽  
Xue Ji
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spectral Characteristics ◽  
Attention Mechanism ◽  
Image Resolution ◽  
Feature Representation ◽  
Local Context ◽  
Context Aware ◽  
Multi Scale ◽  
Feature Information

Extracting road information from high-resolution remote sensing images (HRI) can provide crucial geographic information for many applications. With the improvement of remote sensing image resolution, the image data contain more abundant feature information. However, this phenomenon also enhances the spatial heterogeneity between different types of roads, making it difficult to accurately discern the road and non-road regions using only spectral characteristics. To remedy the above issues, a novel residual attention and local context-aware network (RALC-Net) is proposed for extracting a complete and continuous road network from HRI. RALC-Net utilizes a dual-encoder structure to improve the feature extraction capability of the network, whose two different branches take different feature information as input data. Specifically, we construct the residual attention module using the residual connection that can integrate spatial context information and the attention mechanism, highlighting local semantics to extract local feature information of roads. The residual attention module combines the characteristics of both the residual connection and the attention mechanism to retain complete road edge information, highlight essential semantics, and enhance the generalization capability of the network model. In addition, the multi-scale dilated convolution module is used to extract multi-scale spatial receptive fields to improve the model’s performance further. We perform experiments to verify the performance of each component of RALC-Net through the ablation study. By combining low-level features with high-level semantics, we extract road information and make comparisons with other state-of-the-art models. The experimental results show that the proposed RALC-Net has excellent feature representation ability and robust generalizability, and can extract complete road information from a complex environment.

CNN-based Prediction of COVID-19 using Chest CT Images

2021 ◽  
pp. 2250039
Author(s):  
Tanvi Arora
Keyword(s):  
Ct Scan ◽  
Good Accuracy ◽  
Ct Images ◽  
Chest Ct ◽  
X Ray ◽  
Infected People ◽  
The World ◽  
Severity Of The Disease ◽  
Application Programs ◽  
Chest Ct Scan

The coronavirus disease (COVID-19) pandemic that is caused by the SARS-CoV2 has spread all over the world. It is an infectious disease that can spread from person to person. The severity of the disease can be categorized into five categories namely asymptomatic, mild, moderate, severe, and critical. From the reported cases thus, it has been seen that 80% of the cases that test positive with COVID-19 infection have less than moderate complications, whereas 20% of the positive cases develop severe and critical complications. The virus infects the lungs of an individual, therefore, it has been observed that the X-ray and computed tomography (CT) scan images of the infected people can be used by the machine learning-based application programs to predict the presence of the infection. Therefore, in the proposed work, a Convolutional Neural Network model based upon the DenseNet architecture is being used to predict the presence of COVID-19 infection using the CT scan images of the chest. The proposed work has been carried out using the dataset of the CT images from the COVID CT Dataset. It has 349 images marked as COVID-19 positive and 397 images have been marked as COVID-19 negative. The proposed system can categorize the test set images with an accuracy of 91.4%. The proposed method is capable of detecting the presence of COVID-19 infection with good accuracy using the chest CT scan images of the humans.

scholarly journals COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1530 ◽  
Author(s):  
Mohd Asyraf Zulkifley ◽  
Siti Raihanah Abdani ◽  
Nuraisyah Hani Zulkifley
Keyword(s):  
Deep Learning ◽  
Physical Contact ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Multi Scale ◽  
Adversarial Network ◽  
The World ◽  
Spatial Pyramid Pooling ◽  
Targeted Approach ◽  
Spatial Pyramid

COVID-19 is a disease that can be spread easily with minimal physical contact. Currently, the World Health Organization (WHO) has endorsed the reverse transcription-polymerase chain reaction swab test as a diagnostic tool to confirm COVID-19 cases. This test requires at least a day for the results to come out depending on the available facilities. Many countries have adopted a targeted approach in screening potential patients due to the cost. However, there is a need for a fast and accurate screening test to complement this targeted approach, so that the potential virus carriers can be quarantined as early as possible. The X-ray is a good screening modality; it is quick at capturing, cheap, and widely available, even in third world countries. Therefore, a deep learning approach has been proposed to automate the screening process by introducing LightCovidNet, a lightweight deep learning model that is suitable for the mobile platform. It is important to have a lightweight model so that it can be used all over the world even on a standard mobile phone. The model has been trained with additional synthetic data that were generated from the conditional deep convolutional generative adversarial network. LightCovidNet consists of three components, which are entry, middle, and exit flows. The middle flow comprises five units of feed-forward convolutional neural networks that are built using separable convolution operators. The exit flow is designed to improve the multi-scale capability of the network through a simplified spatial pyramid pooling module. It is a symmetrical architecture with three parallel pooling branches that enable the network to learn multi-scale features, which is suitable for cases wherein the X-ray images were captured from all over the world independently. Besides, the usage of separable convolution has managed to reduce the memory usage without affecting the classification accuracy. The proposed method managed to get the best mean accuracy of 0.9697 with a low memory requirement of just 841,771 parameters. Moreover, the symmetrical spatial pyramid pooling module is the most crucial component; the absence of this module will reduce the screening accuracy to just 0.9237. Hence, the developed model is suitable to be implemented for mass COVID-19 screening.

scholarly journals Assessing Automated Machine Learning Service to Detect COVID-19 from X-Ray and CT Images: A Real-Time Smartphone Application Case Study

2020 ◽  
Author(s):  
Mohammad Razib Mustafiz ◽  
Khaled Mohsin
Keyword(s):  
Machine Learning ◽  
Lung Disease ◽  
Real Time ◽  
Web Application ◽  
Medical Image ◽  
Ct Images ◽  
X Ray ◽  
Medical Image Diagnosis ◽  
The World ◽  
Trained Network

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals First Report on the Geologic Occurrence of Natural Na–A Zeolite and Associated Minerals in Cretaceous Mudstones of the Paja Formation of Vélez (Santander), Colombia

Crystals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 218
Author(s):  
Carlos Alberto Ríos-Reyes ◽  
German Alfonso Reyes-Mendoza ◽  
José Antonio Henao-Martínez ◽  
Craig Williams ◽  
Alan Dyer
Keyword(s):  
Natural Zeolite ◽  
Scanning Electron Micrographs ◽  
Total Sulfur ◽  
Zeolite A ◽  
X Ray Diffraction ◽  
X Ray ◽  
Mineral Phases ◽  
The World ◽  
Porosity And Permeability ◽  
First Time

This study reports for the first time the geologic occurrence of natural zeolite A and associated minerals in mudstones from the Cretaceous Paja Formation in the urban area of the municipality of Vélez (Santander), Colombia. These rocks are mainly composed of quartz, muscovite, pyrophyllite, kaolinite and chlorite group minerals, framboidal and cubic pyrite, as well as marcasite, with minor feldspar, sulphates, and phosphates. Total organic carbon (TOC), total sulfur (TS), and millimeter fragments of algae are high, whereas few centimeters and not biodiverse small ammonite fossils, and other allochemical components are subordinated. Na–A zeolite and associated mineral phases as sodalite occur just beside the interparticle micropores (honeycomb from framboidal, cube molds, and amorphous cavities). It is facilitated by petrophysical properties alterations, due to processes of high diagenesis, temperatures up to 80–100 °C, with weathering contributions, which increase the porosity and permeability, as well as the transmissivity (fluid flow), allowing the geochemistry remobilization and/or recrystallization of pre-existing silica, muscovite, kaolinite minerals group, salts, carbonates, oxides and peroxides. X-ray diffraction analyses reveal the mineral composition of the mudstones and scanning electron micrographs show the typical cubic morphology of Na–A zeolite of approximately 0.45 mμ in particle size. Our data show that the sequence of the transformation of phases is: Poorly crystalline aluminosilicate → sodalite → Na–A zeolite. A literature review shows that this is an unusual example of the occurrence of natural zeolites in sedimentary marine rocks recognized around the world.

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