Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

2021 ◽  
Author(s):  
Soumitri Chattopadhyay ◽  
Rohit Kundu ◽  
Pawan Kumar Singh ◽  
Seyedali Mirjalili ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Local Search ◽  
Feature Selection Method ◽  
Selection Method ◽  
X Ray ◽  
Deep Feature ◽  
Sine Cosine Algorithm

scholarly journals Semi-Skipping Layered Gated Unit and Efficient Network: Hybrid Deep Feature Selection Method for Edge Computing in EEG-Based Emotion Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13378-13389
Author(s):  
Muhammad Adeel Asghar ◽  
Muhammad Jamil Khan ◽  
Humayun Shahid ◽  
Mohammad Shorfuzzaman ◽  
Neal Naixue Xiong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Edge Computing ◽  
Emotion Classification ◽  
Deep Feature

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.

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