scholarly journals ANALYZING CHEST X-RAY TO DIFFERENTIATE SYMPTOMS OF COVID-19 THROUGH ML APPROACH

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Deepali R Deshpande ◽  
Raj L Shah ◽  
Anish N Shaha
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Time Constraints ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Social Distancing ◽  
Machine Learning Model ◽  
Chest X Ray

The motive behind the project is to build a machine learning model for detection of Covid-19. Using this model, it is possible to classify images of chest x-rays into normal patients, pneumatic patients, and covid-19 positive patients. This CNN based model will help drastically to save time constraints among the patients. Instead of relying on limited RT-PCR kits, just a simple chest x-ray can help us determine health of the patient. Not only we get immediate results, but we can also practice social distancing norms more effectively.

scholarly journals Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Emirena Garrafa ◽  
Marika Vezzoli ◽  
Marco Ravanelli ◽  
Davide Farina ◽  
Andrea Borghesi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Learning Model ◽  
Hospital Death ◽  
Web Based ◽  
X Ray ◽  
Machine Learning Model ◽  
Chest X Ray ◽  
Blood Analytes ◽  
Second Wave

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validate using a Machine-Learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

scholarly journals COVID-19 Classification of X-ray Images Using Deep Neural Networks

2020 ◽  
Author(s):  
Elisha Goldstein ◽  
Daphna Keidar ◽  
Daniel Yaron ◽  
Yair Shachar ◽  
Ayelet Blass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Receiver Operating Characteristic ◽  
Deep Neural Networks ◽  
Operating Characteristic ◽  
Learning Model ◽  
X Ray ◽  
Machine Learning Model ◽  
Chest X Ray

AbstractBackgroundIn the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts.PurposeThe purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans.Materials and MethodsIn this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.ResultsThe dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.ConclusionDeep Neural Networks can be used to reliably classify CXR images as COVID-19 positive or negative. Moreover, the image embeddings learned by the network can be used to retrieve images with similar lung findings.SummaryDeep Neural Networks and can be used to reliably predict chest X-ray images as positive for coronavirus disease 2019 (COVID-19) or as negative for COVID-19.Key ResultsA machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for coronavirus disease 2019 with accuracy of 89.7%, sensitivity of 87.1% and area under receiver operating characteristic curve of 0.95.A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.

scholarly journals COVID-19 detection using federated machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252573
Author(s):  
Mustafa Abdul Salam ◽  
Sanaa Taha ◽  
Mohamed Ramadan
Keyword(s):  
Machine Learning ◽  
Model Prediction ◽  
Prediction Accuracy ◽  
Activation Function ◽  
Learning Model ◽  
Learning Models ◽  
X Ray ◽  
Machine Learning Model ◽  
Chest X Ray ◽  
Machine Learning Models

The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine learning model requires accessing or transferring whole data to train the model. In recent years, there has been increasing interest in federated machine learning, as it provides an effective solution for data privacy, centralized computation, and high computation power. In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models’ loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

scholarly journals Chest X-Ray findings and visual quantitative assessment of COVID-19 pneumonia

2020 ◽  
Author(s):  
Michaela Cellina ◽  
Marcello Orsi ◽  
Marta Panzeri ◽  
Giulia van der Byl ◽  
Giancarlo Oliva
Keyword(s):  
Severity Score ◽  
Quantitative Assessment ◽  
X Rays ◽  
Imaging Findings ◽  
Rt Pcr ◽  
Disease Evolution ◽  
X Ray ◽  
Radiological Score ◽  
Chest X Ray

Abstract AimTo assess the most common chest X-Ray findings and distribution in patients with confirmed diagnosis of COVID-19; to verify the repeatability of a radiological severity score, based on visual quantitative assessment; to assess the evolution of chest X-Ray findings at follow-up; to evaluate chest X-Ray sensitivity.MethodsWe analysed chest X-Rays at baseline of 110 consecutive COVID-19 patients (79 males, 31 females; mean age: 64±16 years) with RT-PCR confirmation, who presented to our ED.Two radiologists evaluated the imaging findings and distribution.A severity score, based on the extension of lung abnormalities, was assigned by two other radiologists, independently, to the baseline and follow-up X-Rays, executed in 77/110 cases; interobserver agreement was calculated. Chest X-Ray sensitivity was assessed, with RT-PCR as gold standard.ResultsInterobserver agreement was excellent for baseline and follow-up X-Rays (Cohen's K=0.989, p<0.001, Cohen's K=0.985, p<0.001, respectively). The mean score at baseline was 2.87±1.7 for readers 1 and 2. We observed radiological worsening in 52/77 (67%) patients, with significantly higher scores at follow-up (mean score: 4.27±2.15 for reader 1 and 4.28±2.14 for reader 2, respectively); p<0.001.Ground glass opacities were the most common findings (97/110, 88%). Abnormalities showed bilateral involvement in 67/110 (61%), with prevalent peripheral distribution (48/110, 43.5%).The X-Ray sensitivity for the detection of COVID-19 infection was 91%.ConclusionChest X-Ray highlighted imaging findings in line with those previously reported for chest CT. The use of a radiological score can result in clearer communication with Clinicians and a more precise assessment of disease evolution.

scholarly journals Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

MENDEL ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 9-17
Author(s):  
Hiam Alquran ◽  
Mohammad Alsleti ◽  
Roaa Alsharif ◽  
Isam Abu Qasmieh ◽  
Ali Mohammad Alqudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Features ◽  
Automated System ◽  
Rt Pcr ◽  
False Negatives ◽  
X Ray ◽  
Organ Systems ◽  
Wide Range ◽  
Chest X Ray

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

scholarly journals One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

2021 ◽  
Author(s):  
V. N. Manjunath Aradhya ◽  
Mufti Mahmud ◽  
D. S. Guru ◽  
Basant Agarwal ◽  
M. Shamim Kaiser
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Probabilistic Neural Network ◽  
Classification Model ◽  
Superior Performance ◽  
Generalized Regression Neural Network ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

AbstractCoronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.

scholarly journals COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abolfazl Zargari Khuzani ◽  
Morteza Heidari ◽  
S. Ali Shariati
Keyword(s):  
Machine Learning ◽  
Optimal Allocation ◽  
Global Features ◽  
X Ray ◽  
Learning Classifier ◽  
Machine Learning Model ◽  
Chest X Ray ◽  
Hospital Resources ◽  
Efficient Machine ◽  
Automated Machine Learning

AbstractChest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

scholarly journals Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images

2021 ◽  
pp. 294-299
Author(s):  
Snehal R. Sambhe ◽  
Dr. Kamlesh A. Waghmare
Keyword(s):  
Ct Scan ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Aided Diagnosis ◽  
Excessive Risk

As insufficient testing kits are available, the development of new testing kits for detecting COVID remains an open vicinity of research. It’s impossible to test each and every patient suffering from coronavirus symptoms using the traditional method i.e. RT-PCR. This test requires more time to produce results and have less sensitivity. Detecting feasible coronavirus infection using chest X-Ray may also assist quarantine excessive risk sufferers while testing results are disclosed. A learning model can be built based on CT scan images or Chest X-rays of individuals with higher accuracy. This paper represents a computer-aided diagnosis of COVID 19 infection bases on a feature extractor by using CNN models.

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