scholarly journals Data Augmentation using Auxiliary Classifier for Improved Detection of Covid 19

2021 ◽  
Vol 10 (3) ◽  
pp. 209-214
Author(s):  
Lakshmisetty Ruthvik Raj ◽  
◽  
Bitra Harsha Vardhan ◽  
Mullapudi Raghu Vamsi ◽  
Keerthikeshwar Reddy Mamilla ◽  
...  
Keyword(s):  
Data Augmentation ◽  
X Rays ◽  
Convolutional Network ◽  
X Ray ◽  
Learning Network ◽  
X Ray Imaging ◽  
Abnormal Chest ◽  
Short Period ◽  
Chest X Ray ◽  
Deep Learning Network

COVID-19 is a severe and potentially fatal respiratory infection called coronavirus 2 disease (SARS-Co-2). COVID-19 is easily detectable on an abnormal chest x-ray. Numerous extensive studies have been conducted due to the findings, demonstrating how precise the detection of coronas using X-rays within the chest is. To train a deep learning network, such as a convolutional neural network, a large amount of data is required. Due to the recent end of the pandemic, it is difficult to collect many Covid x-ray images in a short period. The purpose of this study is to demonstrate how X-ray imaging (CXR) is created using the Covid CNN model-based convolutional network. Additionally, we demonstrate that the performance of CNNs and various COVID-19 acquisition algorithms can be used to generate synthetic images from data extensions. Alone, with CNN distribution, an accuracy of 85 percent was achieved. The accuracy has been increased to 95% by adding artificial images generated from data. We anticipate that this approach will expedite the discovery of COVID-19 and result in radiological solid programs. We leverage transfer learning in this paper to reduce time complexity and achieve the highest accuracy.

Portable Ventilation/Perfusion Scanning is Useful for Evaluating Clinically Significant Pulmonary Embolism in the ICU Despite Abnormal Chest Radiography

2018 ◽  
Vol 35 (10) ◽  
pp. 1032-1038 ◽  
Author(s):  
Aaron S. Weinberg ◽  
William Chang ◽  
Grace Ih ◽  
Alan Waxman ◽  
Victor F. Tapson
Keyword(s):  
Pulmonary Embolism ◽  
Chest Radiograph ◽  
High Probability ◽  
Hemodynamic Instability ◽  
X Rays ◽  
X Ray ◽  
Abnormal Chest ◽  
Chest X Ray ◽  
Low Probability ◽  
Clinically Significant

Objective: Computed tomography angiography is limited in the intensive care unit (ICU) due to renal insufficiency, hemodynamic instability, and difficulty transporting unstable patients. A portable ventilation/perfusion (V/Q) scan can be used. However, it is commonly believed that an abnormal chest radiograph can result in a nondiagnostic scan. In this retrospective study, we demonstrate that portable V/Q scans can be helpful in ruling in or out clinically significant pulmonary embolism (PE) despite an abnormal chest x-ray in the ICU. Design: Two physicians conducted chart reviews and original V/Q reports. A staff radiologist, with 40 years of experience, rated chest x-ray abnormalities using predetermined criteria. Setting: The study was conducted in the ICU. Patients: The first 100 consecutive patients with suspected PE who underwent a portable V/Q scan. Interventions: Those with a portable V/Q scan. Results: A normal baseline chest radiograph was found in only 6% of patients. Fifty-three percent had moderate, 24% had severe, and 10% had very-severe radiographic abnormalities. Despite the abnormal x-rays, 88% of the V/Q scans were low probability for a PE despite an average abnormal radiograph rating of moderate. A high-probability V/Q for PE was diagnosed in 3% of the population despite chest x-ray ratings of moderate to severe. Six patients had their empiric anticoagulation discontinued after obtaining the results of the V/Q scan, and no anticoagulation was started for PE after a low-probability V/Q scan. Conclusion: Despite the large percentage of moderate-to-severe x-ray abnormalities, PE can still be diagnosed (high-probability scan) in the ICU with a portable V/Q scan. Although low-probability scans do not rule out acute PE, it appeared less likely that any patient with a low-probability V/Q scan had severe hypoxemia or hemodynamic instability due to a significant PE, which was useful to clinicians and allowed them to either stop or not start anticoagulation.

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.

scholarly journals Digital Image Segmentation Resulting from X-Rays of Covid Patients using K-Means and Extraction Features Method

2021 ◽  
Vol 3 (1) ◽  
pp. 25-28
Author(s):  
Dhian Satria Yudha Kartika ◽  
Anita Wulansari ◽  
Hendra Maulana ◽  
Eristya Maya Safitri ◽  
Faisal Muttaqin
Keyword(s):  
Digital Image ◽  
Confusion Matrix ◽  
Health Sector ◽  
X Rays ◽  
Social Psychological ◽  
Uniform Size ◽  
X Ray ◽  
X Ray Imaging ◽  
Standard Quality ◽  
Chest X Ray

The COVID-19 pandemic has significant impact on people's lives such as economic, social, psychological and health conditions. The health sector, which is spearheading the handling of the outbreak, has conducted a lot of research and trials related to COVID-19. Coughing is a common symptoms among humans affected by COVID-19 in earlier stage. The first step when a patient shows symptoms of COVID-19 was to conduct a chest x-ray imaging. The chest x-rayss can be used as a digital image dataset for analysing  the spread of the virus that enters the lungs or respiratory tract. In this study, 864 x-rays  were used as datasets. The images were still raw, taken directly from Covid-19 patients, so there were still a lot of noise. The process to remove unnecessary images would be carried out in the pre-processing stage. The images used as datasets were not mixed with the background which can reduce the value at the next stage. All datasets were made to have a uniform size and pixels to obtain a standard quality and size in order to support the next stage, namely segmentation. The segmentation stage of the x-ray datasets of Covid-19 patients was carried out using the k-means method and feature extraction. The Confusion Matrix method used as testing process. The accuracy value was 78.5%. The results of this testing process were 78.5% of precision value, 78% of recall and  79% for f-measure

scholarly journals Deep Learning Based Hybrid Models for Prediction of COVID-19 using Chest X-Ray

2020 ◽  
Author(s):  
Shree Charran R ◽  
Rahul Kumar Dubey
Keyword(s):  
Data Augmentation ◽  
False Negative ◽  
X Ray ◽  
Infected Case ◽  
X Ray Imaging ◽  
Normal Chest ◽  
Chest X Ray ◽  
To Come ◽  
Spread Of Infection

COVID-19 has ended up being the greatest pandemic to come to pass for on humanity in the last century. It has influenced all parts of present day life. The best way to confine its spread is the early and exact finding of infected patients. Clinical imaging strategies like Chest X-ray imaging helps specialists to assess the degree of spread of infection. In any case, the way that COVID-19 side effects imitate those of conventional Pneumonia brings few issues utilizing of Chest Xrays for its prediction accurately. In this investigation, we attempt to assemble 4 ways to deal with characterize between COVID-19 Pneumonia, NON-COVID-19 Pneumonia, and an Healthy- Normal Chest X-Ray images. Considering the low accessibility of genuine named Chest X-Ray images, we incorporated combinations of pre-trained models and data augmentation methods to improve the quality of predictions. Our best model has achieved an accuracy of 99.5216%. More importantly, the hybrid did not predict a False Negative Normal (i.e. infected case predicted as normal) making it the most attractive feature of the study.

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

2020 ◽  
pp. 2151004 ◽  
Author(s):  
Dilbag Singh ◽  
Vijay Kumar ◽  
Vaishali Yadav ◽  
Manjit Kaur
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Automated Analysis ◽  
X Rays ◽  
X Ray ◽  
Screening Model ◽  
X Ray Imaging ◽  
Adaptive Differential Evolution ◽  
Chest X Ray ◽  
Almost All

There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

Radiological diagnosis of aspirated foreign bodies in children: Review of 343 cases

1990 ◽  
Vol 104 (10) ◽  
pp. 778-782 ◽  
Author(s):  
Liancai Mu ◽  
Deqiang Sun ◽  
Ping He
Keyword(s):  
Foreign Body ◽  
Foreign Bodies ◽  
Correct Diagnosis ◽  
X Rays ◽  
Endoscopic Removal ◽  
X Ray ◽  
Abnormal Chest ◽  
Mediastinal Shift ◽  
Plain Chest ◽  
Chest X Ray

AbstractIn our series of 400 Chinese children with foreign body aspiration (FBA),343 cases were evaluated by fluoroscopy and/or plain chest X-rays before endoscopic removal of the foreign bodies. The majority of the foreign bodies (FBs) were organic (378/400, 94.5 per cent). The results showed that mainstem bronchial foreign bodies were diagnosed correctly in 68 per centof cases compared with 65 per cent correct diagnoses with segmental bronchial foreign bodies, but only 22 per cent correct diagnoses with tracheal, and 0 per cent correct diagnosis in those with laryngeal foreign bodies. Eighty per cent (32/40) of the children with laryngotracheal FBs had normal X-ray findings, whereas 67.7 per cent (205/303) of the children with bronchial FBs had abnormal chest X-ray findings. The most common positive radiological signs in the children with tracheobronchial FBs were obstructive emphysema (131/213, 62 per cent) and mediastinal shift (117/213, 55 percent). The incidence of major complications was related not only to the size of the foreign body and its location but also the duration since aspiration. The most common types of bronchial obstructions by airway FBs are discussed.

scholarly journals Chest X-Ray Findings and Temporal Lung Changes in Patients with COVID-19 Pneumonia

2020 ◽  
Author(s):  
Liqa A Rousan ◽  
Eyhab Elobeid ◽  
Musaab Karrar ◽  
Yousef Khader
Keyword(s):  
Ct Scan ◽  
Chest Ct ◽  
Common Finding ◽  
X Rays ◽  
Radiographic Findings ◽  
X Ray ◽  
Abnormal Chest ◽  
Chest X Ray ◽  
Chest Ct Scan

Abstract Background: Chest CT scan and chest x-rays show characteristic radiographic findings in patients with COVID-19 pneumonia. Chest x-ray can be used in diagnosis and follow up in patients with COVID-19 pneumonia. The study aims at describing the chest x-ray findings and temporal radiographic changes in COVID-19 patients.Methods: From March 15 to April 20, 2020 patients with positive reverse transcription polymerase chain reaction (RT-PCR) for COVID-19 were retrospectively studied. Patients’ demographics, clinical characteristics, and chest x-ray findings were reported. Radiographic findings were correlated with the course of the illness and patients’ symptoms.Results: A total of 88 patients (50 (56.8%) females and 38 (43.2%) males) were admitted to the hospital with confirmed COVID-19 pneumonia. Their age ranged from 3-80 years (35.2 ±18.2 years). 48/88 (45%) were symptomatic, only 13/88 (45.5%) showed abnormal chest x-ray findings. A total of 190 chest x-rays were obtained for the 88 patients with a total of 59/190 (31%) abnormal chest x-rays. The most common finding on chest x-rays was peripheral ground glass opacities (GGO) affecting the lower lobes. In the course of illness, the GGO progressed into consolidations peaking around 6-11 days (GGO 70%, consolidations 30%). The consolidations regressed into GGO towards the later phase of the illness at 12-17 days (GGO 80%, consolidations 10%). There was increase in the frequency of normal chest x-rays from 9% at days 6- 11 up to 33% after 18 days indicating a healing phase. The majority (12/13, 92.3%) of patients with abnormal chest x-rays were symptomatic (P=0.005).Conclusion: The chest x-ray findings were similar to those reported on chest CT scan in patients with COVID-19, Chest x-ray can be used in diagnosis and follow up in patients with COVID-19 pneumonia.

scholarly journals Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images

2020 ◽  
Vol 13 (1) ◽  
pp. 8
Author(s):  
Sagar Kora Venu ◽  
Sridhar Ravula
Keyword(s):  
Data Augmentation ◽  
Medical Condition ◽  
Training Data ◽  
Superior Performance ◽  
Generative Adversarial Networks ◽  
X Rays ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Augmentation Techniques ◽  
Chest X Ray

Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired results as they often over-fit the majority class samples’ data. Data augmentation is often performed on the training data to address the issue by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue to increase the dataset sizes. Radiologists generally use chest X-rays for the diagnosis of pneumonia. Due to patient privacy concerns, access to such data is often protected. In this study, we performed data augmentation on the Chest X-ray dataset to generate artificial chest X-ray images of the under-represented class through generative modeling techniques such as the Deep Convolutional Generative Adversarial Network (DCGAN). With just 1341 chest X-ray images labeled as Normal, artificial samples were created by retaining similar characteristics to the original data with this technique. Evaluating the model resulted in a Fréchet Distance of Inception (FID) score of 1.289. We further show the superior performance of a CNN classifier trained on the DCGAN augmented dataset.

scholarly journals Diagnosing heart failure from chest X-ray images using deep learning

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Matsumoto ◽  
S Kodera ◽  
H Shinohara ◽  
A Kiyosue ◽  
Y Higashikuni ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
Funding Source ◽  
X Rays ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Prediction Probability ◽  
Chest X Ray

Abstract   The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

scholarly journals DenseCapsNet: Detection of COVID-19 X-ray Images Using a Capsule Network

2020 ◽  
Author(s):  
Hao Quan ◽  
Xiaosong Xu ◽  
Tingting Zheng ◽  
Zhi Li ◽  
Mingfang Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Augmentation ◽  
Detection Performance ◽  
Convolution Neural Network ◽  
X Ray ◽  
Learning Framework ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Novel Coronavirus

Abstract Objective: A deep learning framework for detecting COVID-19 is developed, and a small amount of chest X-ray data is used to accurately screen COVID-19.Methods: In this paper, we propose a deep learning framework that integrates convolution neural network and capsule network. DenseNet and CapsNet fusion are used to give full play to their respective advantages, reduce the dependence of convolution neural network on a large amount of data, and can quickly and accurately distinguish COVID-19 from Non-COVID-19 through chest X-ray imaging.Results: A total of 1472 chest X-ray COVID-19 and non-COVID-19 images are used, this method can achieve an accuracy of 99.32% and a precision of 100%, with 98.55% sensitivity and 100% specificity.Conclusion: These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia X-ray detection. We also prove through experiments that the detection performance of DenseCapsNet is not affected fundamentally by a lack of data augmentation and pre-training.

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