scholarly journals Fast automated detection of COVID-19 from medical images using convolutional neural networks

2020 ◽  
Author(s):  
Shuang Liang ◽  
Huixiang Liu ◽  
Yu Gu ◽  
Xiuhua Guo ◽  
Hongjun Li ◽  
...  
Keyword(s):  
Medical Images ◽  
Strong Correlations ◽  
Clinical Indicators ◽  
Learning Framework ◽  
Testing Method ◽  
Classification Framework ◽  
Global Pandemic ◽  
Diagnosis Method ◽  
Aided Diagnosis ◽  
Significant Health

Abstract Coronavirus Disease 2019 (COVID-19) is a global pandemic that poses significant health risks. The sensitivity of diagnostic tests for COVID-19 is low due to irregularities in the handling of the specimens. We propose a deep learning framework that identifies COVID-19 from medical images as an effective auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography (CT) images to train and evaluate the convolutional neural network (CNN). The CNN achieves a performance similar to that of experts and provides high scores for multiple statistical indices, with F1 scores above 96% and specificity over 99%. Heatmaps are used to visualize the salient features extracted by the CNN. The CNN-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, improving the interpretation accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.

scholarly journals Fast automated detection of COVID-19 from medical images using convolutional neural networks

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shuang Liang ◽  
Huixiang Liu ◽  
Yu Gu ◽  
Xiuhua Guo ◽  
Hongjun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Medical Images ◽  
Clinical Indicators ◽  
Specimen Handling ◽  
Learning Framework ◽  
Computed Tomography Images ◽  
Classification Framework ◽  
Global Pandemic ◽  
The Neural Network ◽  
Diagnosis Method

AbstractCoronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

scholarly journals Literature Review of the Implications of Exercise Rehabilitation Strategies for SARS Patients on the Recovery of COVID-19 Patients

Healthcare ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 590
Author(s):  
Wei Cui ◽  
Ting Ouyang ◽  
Ye Qiu ◽  
Di Cui
Keyword(s):  
Exercise Intervention ◽  
Treatment Strategies ◽  
Severe Pneumonia ◽  
Clinical Indicators ◽  
Exercise Rehabilitation ◽  
Global Pandemic ◽  
Epidemic Period ◽  
Local Risk ◽  
And Control ◽  
Rehabilitation Strategies

As a global pandemic, COVID-19 shows no sign of letting up. With the control of the epidemic in China, the proportion of patients with severe and critical diseases being cured and discharged from hospital has increased, and the recovery of COVID-19 patients has become an important issue that urgently needs attention and solutions. By summarizing the exercise rehabilitation strategies and progress of SARS in 2003, this paper analyzed the differences in clinical indicators and recovery characteristics of severe pneumonia caused by the two viruses, and provided comprehensive exercise guidance and intervention strategies for COVID-19 patients for rehabilitation and nursing by referring to the problems and treatment strategies in the rehabilitation and nursing work of SARS. In the post-epidemic period, China will build a multi-dimensional epidemic prevention system by improving the effectiveness of mass training and strengthening local risk prevention and control. This paper discusses the exercise rehabilitation strategy of SARS patients after recovery, which has guiding significance for exercise intervention and scientific fitness of COVID-19 patients after recovery during epidemic prevention period.

FEATURES OF THE COURSE AND TACTICS OF MANAGING CHILDREN WITH KIDNEYS AND URINARY TRACT DISEASES DURING THE PANDEMIC OF NEW CORONAVIRUS INFECTION COVID-19

2021 ◽  
Vol 100 (4) ◽  
pp. 74-79
Author(s):  
I.M. Kagantsov ◽  
◽  
V.V. Sizonov ◽  
V.G. Svarich ◽  
K.P. Piskunov ◽  
...  
Keyword(s):  
Age Groups ◽  
Clinical Manifestations ◽  
Routine Care ◽  
The Novel ◽  
Urinary System ◽  
Death Rates ◽  
Global Pandemic ◽  
Coronavirus Infection ◽  
Novel Coronavirus ◽  
Significant Health

The novel coronavirus infection (SARS-CoV-2), which first appeared in Wuhan, China in December 2019, has been declared a global pandemic by WHO. COVID-19 affects people of all age groups. The disease in children is usually asymptomatic or mild compared to adults, and with a significantly lower death rates. Data on kidney damage in children with COVID-19, as well as the effect of coronavirus infection on the course of diseases of the genitourinary system, are limited, the risks of contracting a new coronavirus infection in children with significant health problems, including those with chronic kidney disease, remain uncertain. The pandemic has affected the activities of surgeons treating diseases of the urinary system in children. Since the prospects for the end of the pandemic are vague, it is necessary to formulate criteria for selecting patients who can and should be provided with routine care in the pandemic. The purpose of this review is to highlight the features of the clinical manifestations and treatment of children with COVID-19, occurring against the background of previous renal pathology or complicating its course.

scholarly journals Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

2021 ◽  
Author(s):  
Farshad Saberi-Movahed ◽  
Mahyar Mohammadifard ◽  
Adel Mehrpooya ◽  
Mohammad Rezaei-Ravari ◽  
Kamal Berahmand ◽  
...  
Keyword(s):  
Feature Selection ◽  
Poor Prognosis ◽  
Clinical Decision Making ◽  
Unmet Need ◽  
Clinical Decision ◽  
Clinical Indicators ◽  
Arterial Blood Gas ◽  
Clinical Biomarkers ◽  
Arterial Blood ◽  
Global Pandemic

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Ma- trix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Tiled Sparse Coding in Eigenspaces for Image Classification

2021 ◽  
Author(s):  
Juan E Arco ◽  
Andrés Ortiz ◽  
Javier Ramírez ◽  
Yu-Dong Zhang ◽  
Juan M Górriz
Keyword(s):  
Sparse Coding ◽  
Computational Cost ◽  
Learning Approaches ◽  
Classification Framework ◽  
Large Databases ◽  
Class Context ◽  
Control Versus ◽  
Complex Solutions ◽  
Aided Diagnosis ◽  
High Computational Cost

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. blackThese alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are blackfirst partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. blackThen, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. blackOur system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

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