Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can b...Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.展开更多
The coronavirus disease 2019(COVID-19)pandemic presents a significant global public health challenge.One in five individuals with COVID-19 presents with symptoms that last for weeks after hospital discharge,a conditio...The coronavirus disease 2019(COVID-19)pandemic presents a significant global public health challenge.One in five individuals with COVID-19 presents with symptoms that last for weeks after hospital discharge,a condition termed“long COVID”.Thus,efficient follow-up of patients is needed to assess the resolution of lung pathologies and systemic involvement.Thoracic imaging is multimodal and involves using different forms of waves to produce images of the organs within the thorax.In general,it includes chest X-ray,computed tomography,lung ultrasound and magnetic resonance imaging techniques.Such modalities have been useful in the diagnosis and prognosis of COVID-19.These tools have also allowed for the follow-up and assessment of long COVID.This review provides insights on the effectiveness of thoracic imaging techniques in the follow-up of COVID-19 survivors who had long COVID.展开更多
Objective：Thoracic injuries are responsible for 25% of deaths of blunt traumas.Chest X-ray （CXR） is the first diagnostic method in patients with blunt trauma.The aim of this study was to detect the accuracy of CXR ...Objective：Thoracic injuries are responsible for 25% of deaths of blunt traumas.Chest X-ray （CXR） is the first diagnostic method in patients with blunt trauma.The aim of this study was to detect the accuracy of CXR versus chest computed tomograpgy （CT） in hemodynamically stable patients with blunt chest trauma.Methods：Study was conducted at the emergency department of S ina Hospital from March 2011 to March 2012.Hemodynamically stable patients with at least 16 years of age who had blunt chest trauma were included.All patients underwent the same diagnostic protocol which consisted of physical examination,CXR and CT scan respectively.Results：Two hundreds patients （84% male and 16% female） were included with a mean age of（37.9±13.7） years.Rib fracture was the most common finding of CXR （12.5%） and CT scan （25.5%）.The sensitivity of CXR for hemothorax,thoracolumbar vertebra fractures and rib fractures were 20%,49% and 49%,respectively.Pneumothorax,foreign body,emphysema,pulmonary contusion,liver hematoma and sternum fracture were not diagnosed with CXR alone.Conclusion：Applying CT scan as the first-line diagnostic modality in hemodynamically stable patients with blunt chest trauma can detect pathologies which may change management and outcome.展开更多
文摘Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.
文摘The coronavirus disease 2019(COVID-19)pandemic presents a significant global public health challenge.One in five individuals with COVID-19 presents with symptoms that last for weeks after hospital discharge,a condition termed“long COVID”.Thus,efficient follow-up of patients is needed to assess the resolution of lung pathologies and systemic involvement.Thoracic imaging is multimodal and involves using different forms of waves to produce images of the organs within the thorax.In general,it includes chest X-ray,computed tomography,lung ultrasound and magnetic resonance imaging techniques.Such modalities have been useful in the diagnosis and prognosis of COVID-19.These tools have also allowed for the follow-up and assessment of long COVID.This review provides insights on the effectiveness of thoracic imaging techniques in the follow-up of COVID-19 survivors who had long COVID.
文摘Objective：Thoracic injuries are responsible for 25% of deaths of blunt traumas.Chest X-ray （CXR） is the first diagnostic method in patients with blunt trauma.The aim of this study was to detect the accuracy of CXR versus chest computed tomograpgy （CT） in hemodynamically stable patients with blunt chest trauma.Methods：Study was conducted at the emergency department of S ina Hospital from March 2011 to March 2012.Hemodynamically stable patients with at least 16 years of age who had blunt chest trauma were included.All patients underwent the same diagnostic protocol which consisted of physical examination,CXR and CT scan respectively.Results：Two hundreds patients （84% male and 16% female） were included with a mean age of（37.9±13.7） years.Rib fracture was the most common finding of CXR （12.5%） and CT scan （25.5%）.The sensitivity of CXR for hemothorax,thoracolumbar vertebra fractures and rib fractures were 20%,49% and 49%,respectively.Pneumothorax,foreign body,emphysema,pulmonary contusion,liver hematoma and sternum fracture were not diagnosed with CXR alone.Conclusion：Applying CT scan as the first-line diagnostic modality in hemodynamically stable patients with blunt chest trauma can detect pathologies which may change management and outcome.
