scholarly journals A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images

2021 ◽  
Author(s):  
Hamid Nasiri ◽  
Seyyed Ali Alavi
Keyword(s):  
Feature Selection ◽  
Predictive Accuracy ◽  
False Negative ◽  
Feature Selection Method ◽  
Selection Method ◽  
Rt Pcr ◽  
X Ray ◽  
Negative Results ◽  
Difficulty Breathing ◽  
Chest X Ray

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia). <br>

scholarly journals A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images

2021 ◽  
Author(s):  
Hamid Nasiri ◽  
Seyyed Ali Alavi
Keyword(s):  
Feature Selection ◽  
Predictive Accuracy ◽  
False Negative ◽  
Feature Selection Method ◽  
Selection Method ◽  
Rt Pcr ◽  
X Ray ◽  
Negative Results ◽  
Difficulty Breathing ◽  
Chest X Ray

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).

scholarly journals A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images

2021 ◽  
Author(s):  
Hamid Nasiri ◽  
Seyyed Ali Alavi
Keyword(s):  
Feature Selection ◽  
Predictive Accuracy ◽  
False Negative ◽  
Feature Selection Method ◽  
Selection Method ◽  
Rt Pcr ◽  
X Ray ◽  
Negative Results ◽  
Difficulty Breathing ◽  
Chest X Ray

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia). <br>

scholarly journals A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Hamid Nasiri ◽  
Seyed Ali Alavi
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
False Negative ◽  
Feature Selection Method ◽  
Multiclass Classification ◽  
Selection Method ◽  
Gradient Boosting ◽  
X Ray ◽  
Extreme Gradient Boosting ◽  
Chest X Ray

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people’s everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus’s transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method’s precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.

COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

2020 ◽  
Author(s):  
Kiran Purohit ◽  
Abhishek Kesarwani ◽  
Dakshina Ranjan Kisku ◽  
Mamata Dalui
Keyword(s):  
Ct Scan ◽  
False Negative ◽  
Radical Change ◽  
Testing Time ◽  
Rt Pcr ◽  
X Ray ◽  
Negative Results ◽  
False Negative Results ◽  
Chest X Ray ◽  
Coronavirus Infection

AbstractCOVID-19 is posed as very infectious and deadly pneumonia type disease until recent time. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect coronavirus infection. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting coronavirus infection in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of coronavirus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation achieves sensitivity of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.

scholarly journals A New Feature Selection Method for Text Classification Based on Independent Feature Space Search

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yong Liu ◽  
Shenggen Ju ◽  
Junfeng Wang ◽  
Chong Su
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Predictive Accuracy ◽  
Feature Selection Method ◽  
Feature Space ◽  
Selection Method ◽  
The Other ◽  
Feature Subset ◽  
Search Range ◽  
Text Documents

Feature selection method is designed to select the representative feature subsets from the original feature set by different evaluation of feature relevance, which focuses on reducing the dimension of the features while maintaining the predictive accuracy of a classifier. In this study, we propose a feature selection method for text classification based on independent feature space search. Firstly, a relative document-term frequency difference (RDTFD) method is proposed to divide the features in all text documents into two independent feature sets according to the features’ ability to discriminate the positive and negative samples, which has two important functions: one is to improve the high class correlation of the features and reduce the correlation between the features and the other is to reduce the search range of feature space and maintain appropriate feature redundancy. Secondly, the feature search strategy is used to search the optimal feature subset in independent feature space, which can improve the performance of text classification. Finally, we evaluate several experiments conduced on six benchmark corpora, the experimental results show the RDTFD method based on independent feature space search is more robust than the other feature selection methods.

scholarly journals A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

2019 ◽  
Vol 9 (3) ◽  
pp. 437 ◽  
Author(s):  
Shen Su ◽  
Yanbin Sun ◽  
Xiangsong Gao ◽  
Jing Qiu ◽  
Zhihong Tian
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
False Negative ◽  
Feature Selection Method ◽  
Window Size ◽  
Selection Method ◽  
Sensor Data ◽  
Data Correlation ◽  
The Right ◽  
Sensor Clustering

Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.

scholarly journals COVID-19 – the challenge to treat a disease and not a positive RT-PCR test

Pharmacia ◽  
2021 ◽  
Vol 68 (1) ◽  
pp. 155-161
Author(s):  
Maria Georgieva Moneva-Sakelarieva ◽  
Yozlem Ali Kobakova ◽  
Petar Yordanov Atanasov ◽  
Danka Petrova Obreshkova ◽  
Stefka Achkova Ivanova ◽  
...  
Keyword(s):  
False Negative ◽  
Rapid Test ◽  
Rt Pcr ◽  
Negative Results ◽  
False Negative Results ◽  
Chest X Ray ◽  
Pulmonary Changes ◽  
Coagulation Status ◽  
Pcr Test

The new pandemic disease COVID is quick spread worldwide.The primary method used for diagnosing of COVID-19 is detecting viral nucleic acids. The main problem with RT-PCR test is the false negative results. The negative RT-PCR does not exclude a SARS-CoV-2 infection and this method should not be used as the only diagnostic criteria. The RT-PCR result does not change the complex treatment of the disease. The aim of the current study is to compare the four groups clinical cases of the different parameters: RT-PCR test, rapid test, clinical picture, laboratory tests as hematology, inflammatory markers, coagulation status and chemistry and imaging examinations: Chest X-ray at and Chest CT scan. Complex therapeutic approach has been implemented: antibiotic, inflammatory, anticoagulants, oxygen therapy, hepatoprotectors, antimycotics, fibrinolytics, probiotics, essential oils, vitamins. During the follow-up period, a tendency for significant reduction and resorption of the pulmonary changes on the CT scans has been seen.

scholarly journals COVID-19—Clinical Relevance of a Negative RT-PCR SARS-CoV-2 Test

2020 ◽  
Vol 2020 (1) ◽  
pp. 1
Author(s):  
Roxana Jurca ◽  
Camil Mihuța ◽  
Emanuela Tudorache ◽  
Diana Manolescu ◽  
Cristian Oancea
Keyword(s):  
False Negative ◽  
Test Methods ◽  
Rt Pcr ◽  
Clinical Criteria ◽  
Reactive Protein ◽  
Negative Results ◽  
Systemic Corticosteroids ◽  
False Negative Results ◽  
Chest X Ray ◽  
Diagnosis Accuracy

(1) Background: In the current clinical practice of the COVID-19 infection, the focus should not be placed on the positive RT-PCR results, but rather on the epidemiological, clinical, and imaging aspects specific to the disease. (2) Case Report: We present the case of a 34-year-old female, who had contacts with both her parents, both of whom were confirmed to have SARS-CoV-2 infection by RT-PCR testing. She presented for about one week symptoms suggestive of COVID-19 infection, determining her to repeatedly go to the emergency room, where she had three negative SARS-CoV-2 RT-PCR tests. The blood tests revealed leukopenia with lymphocytopenia, with increased lactate dehydrogenase (LDH) and C-reactive protein (CRP). Moreover, the chest X-ray showed modifications specific for COVID-19, and the diagnosis of COVID-19 was set. Drug treatment with hydroxychloroquine, azithromycin, cephalosporins, systemic corticosteroids, anticoagulants, bronchodilators, and interleukin-6 inhibitors was initiated, together with oxygen therapy. (3) Discussion: SARS-CoV-2 RT-PCR testing may give false negative results due to inadequate biological sampling, or to the accuracy of the test methods. A significant contribution to the diagnosis is made by the specific computed tomography (CT) criteria of COVID-19. (4) Conclusions: A priority for COVID-19 diagnosis accuracy is epidemiological investigation, together with clinical criteria and CT imaging, even in the presence of a negative RT-PCR test.

scholarly journals Persistent Hiccups as atypical presentation of COVID-19: a Case Report

2020 ◽  
Author(s):  
Mohamed Zahran
Keyword(s):  
Early Recognition ◽  
Viral Transmission ◽  
Atypical Presentation ◽  
Shortness Of Breath ◽  
Rt Pcr ◽  
X Ray ◽  
Recent Outbreak ◽  
Difficulty Breathing ◽  
Chest X Ray ◽  
Atypical Presentations

Abstract The recent outbreak of SARS-CoV-2 has become pandemic since it began in late 2019. Typical symptoms include cough, shortness of breath or difficulty breathing, fever, myalgia and sore throat. There are other unusual or atypical presentations of COVID-19 in ORL practice. We report a 64-year-old male patient presenting with hiccups as the only symptom. Chest x-ray ray revealed new ground-glass opacities in both lung fields and he was found to be COVID-19 positive by RT-PCR. Early recognition of the COVID-19 atypical presentations by the Otolaryngologist facilitates subsequent management and case isolation to eliminate the risk of viral transmission.

Sign in / Sign up

Export Citation Format

Share Document