Covid-19 Detection by Machine Learning Using Chest Radiographs (original) (raw)
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Automatic Covid-19 Infected Chest X-Ray Image Classification using Support Vector Machine
International Journal of Trend in Scientific Research and Development, 2021
The recent coronavirus disease (COVID-19) is extending very speedily over the world for the sake of its very infectious nature and is announced nationwide by the world health organization group of coronavirus that has caused pani people through the sneezing and coughing of the infected person and weakens the person and it then slowly infects the affected person’s lungs. In this study, we have classified the chest X infected chest images or normal chest images. Classifying the chest X images is hard and time-consuming work for human beings. Hence, an automatic Covid-19 infected chest X classification tool is very useful even for experience humans t of chest X-Ray images. For that, we have proposed a new machine learning technique to automatically classify the chest Covid images or normal chest images. Hence, we have used a Machine learning (ML) model like Support Vector Machine infected chest images and normal chest images. For this work, at first, we have preprocessed the chest X-Ra...
Computers, Materials & Continua
The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor (k-NN), Decision Tree (DT), and CN 2 rule inducer techniques) and deep learning models (e.g., MobileNets V2, ResNet50, GoogleNet, DarkNet and Xception). A large X-ray dataset has been created and developed, namely the COVID-19 vs. Normal (400 healthy cases, and 400 COVID cases). To the best of our knowledge, it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases. Based on the results obtained from the experiments, it can be concluded that all the models performed well, deep learning models had achieved the optimum accuracy of 98.8% in ResNet50 model. In comparison, in traditional machine learning techniques, This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 3290 CMC, 2021, vol.66, no.3 the SVM demonstrated the best result for an accuracy of 95% and RBF accuracy 94% for the prediction of coronavirus disease 2019.
COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm
Cluster Computing, 2023
As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage. Keywords COVID-19 Á The severity of infection Á Mild stage Á Moderate stage Á Severe stage Á SVM Á Decision tree Á KNN Á Naïve Bayes Á Segmentation Á CT scans
X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques
Computer Systems Science and Engineering
In early December 2019, the city of Wuhan, China, reported an outbreak of coronavirus disease (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). On January 30, 2020, the World Health Organization (WHO) declared the outbreak a global pandemic crisis. In the face of the COVID-19 pandemic, the most important step has been the effective diagnosis and monitoring of infected patients. Identifying COVID-19 using Machine Learning (ML) technologies can help the health care unit through assistive diagnostic suggestions, which can reduce the health unit's burden to a certain extent. This paper investigates the possibilities of ML techniques in identifying/ detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection. This approach includes preprocessing, feature extraction, and classification. However, the features are extracted using the Histogram of Oriented (HOG) and Local Binary Pattern (LBP) feature descriptors. Furthermore, for the extracted features classification, six ML models of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) is used. Experimental results show that the diagnostic accuracy of random forest classifier (RFC) on extracted HOG plusLBP features is as high as 94% followed by SVM at 93%. The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%. Overall, the predicted approach has shown higher classification accuracy and effective diagnostic performance. It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.
Early Covid 19 Detection with Chest X-Ray Image Using Machine Learning
2021
The emergence of SARS-COV2 from Wuhan at the end of December 2019 has spread to 200 countries is the leading cause of deaths. Belonging to be β-COV with single-strand RNA attacks the human respiratory system. The COVID-19 symptoms appear after incubation. The appearance of symptoms varies depending on the age and status of the immune system. Globally, current trials are on vaccines and focused on plasma therapy with the survivor’s plasma. The image processing technologies has been implemented to find out the corona using chest X-ray image. The identification of the features co-ordinates will be undergone with machine learning sub domain an emerging technology. Logistic regression classification classifies the feature which are all been used there.
Bioscience Biotechnology Research Communications, 2021
At recent times as the COVID 19 pandemic surges in global level, this research article aims at presenting an efficient support system for the physicians in diagnosing the COVID 19 disease using deep learning architectures. The automated diagnosis is made available primarily based on evaluation of medical images (Chest CT images) in diagnosing COVID-19. This COVID 19 affects the normal functioning of the lungs and it damages the tiny air sacs called alveoli. The Chest CT, especially in case of diagnosis of severely infected patients has higher importance and also for immediate COVID 19 screening before certain emergency surgeries and treatment procedures. Till now, the diagnosis of chest CT depends on the visual analysis of radiologists, which may be prone to error at times. First of all, chest CT holds hundreds of slices, which takes a while to diagnose. Next, COVID-19, as a pulmonary disease, has a similar instance with diverse varieties of pneumonia. This research attempts to diagnose the severity of COVID-19 by detecting the abnormalities based on the radiomics features of the chest CT images (pre-processed). These features help to discriminate between the normal opaque region, GGO's, and high intensity region including blood vessels and other consolidations. This (classification of chest CT image using radiomic features for COVID 19 diagnosis using neural network) approach can lessen subjective variability and improves diagnostic efficiency when compared to modern-day qualitative evaluation techniques.
IJCRT - International Journal of Creative Research Thoughts (IJCRT), 2021
Corona virus Disease (COVID19) is a fast-spreading infectious disease that is currently causing a healthcare crisis around the world. Due to the current limitations of the reverse transcription-polymerase chain reaction (RT-PCR) based tests for detecting COVID19, recently radiology imaging based ideas have been proposed by various works. Recognition of COVID-19 in Chest CT Scan images is a challenging task. Identification of disease in a human organ demands expert's opinion and the patients medications are completely dependent on the results given by that expert. However, there might be situations where experts may not be available or too busy. To tackle the emergencies, which arise due to lack of experts, it is necessary to screen inputs image of Chest CT to classify whether the chest CT scan image is COVID-19 infectious or non-infectious i.e. healthy. In this paper, we present a taxonomy of the state-of-the-art machine learning based disease detection systems. This research aims to survey a Machine-Learning-System (MLS) to detect the COVID-19 infection using the CT scan Slices (CTS) with effective feature extraction by various researchers.
Artificial intelligent techniques applied for detection COVID-19 based on chest medical imaging
TELKOMNIKA, 2022
One of the ways to detect coronavirus disease of 2019 (COVID-19) is X-rays, computerized tomography (CT). This paper aims to detect COVID-19 from CT images without any user intervention. The proposed algorithm consists of 5 stages. These stages include; the first stage aims to collect data from hospitals and internet websites, the second stage is pre-processing stage to remove noise and convert it from red green blue (RGB) to grayscale and then improve image quality, the third is the segmentation stage which included threshold and region-growing segmentation methods. The fourth stage is used to extract important characteristics, and the last stage is classification CT images using feed forward back propagation network (FFBPN) and support vector machines (SVM) and compare the results between them and see if the person is infected or healthy. This study was implemented in MATLAB software. The results showed that the noise cancellation technology using anisotropic filtering gave the best results. Region-growing method was reliable to separate COVID-19 infected from healthy regions. The FFBPN has given the best results for detecting and classifying COVID-19. The results of the proposed methodology are rapid and accurate in detecting COVID-19. The output from classifier is displayed on the Rasbperry Pi that included weather if patient is infected or not and the severity of COVID-19 infection.
COVID-19 Disease Detection Based on Machine Learning and Chest X-Ray Images
UHD Journal of Science and Technology, 2022
Due to increasing population, automated illness identification has become a critical problem in medical research. An automated illness detection framework aids physicians in disease diagnosis by providing precise, consistent, and quick findings, as well as lowering the mortality rate. Coronavirus (COVID-19) has expanded worldwide and is now one of the most severe and acute disorders. To avoid COVID-19 from spreading, making an automatic detection system based on X-ray chest pictures ought to be the quickest diagnostic alternative. The goal of this research is to come up with the best model for detecting COVID-19 diagnosis with the greatest accuracy. Therefore, four models, Convolutional Neural Networks, Residual Network 50, Visual Geometry Group 16 (VGG16), and VGG19, have been evaluated using the same images preprocessing method. In this study, performance metrics include accuracy, precision, recall, and F1 scores are used for evaluating proposed method. According to our findings, ...