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COVID-19 Detection from chest x-ray images Using Convolutional Neural Network Approach

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Journal of Computer and Communications 2023年第5期11卷 29-41页

作者： Md. Harun Or Rashid Muzakkir Hossain Minhaz Ananya Sarker Must. Asma Yasmin Md. Golam An Nihal Department of Computer Science and Engineering Bangladesh Army University of Engineering & Technology (BAUET) Qadirabad Cantonment Natore Bangladesh

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. chest x-ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest x-ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest x-ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

关键词： COVID-19 chest x-ray images CNN Virus Accuracy

A COVID-19 Detection Model Based on Convolutional Neural Network and Residual Learning

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Computers, Materials & Continua 2023年第5期75卷 3625-3642页

作者： Bo Wang Yongxin Zhang Shihui Ji Binbin Zhang xiangyu Wang Jiyong Zhang Luoyang Normal University Luoyang471934China Southwest Jiaotong University Chengdu611756China

Amodel that can obtain rapid and accurate detection of coronavirus disease 2019(COVID-19)plays a significant role in treating and preventing the spread of disease ***,designing such amodel that can balance the detection accuracy andweight parameters ofmemorywell to deploy a mobile device is *** this point into account,this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model,which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and *** convolutional neural network can extract the COVID-19 feature information by repeated convolutional *** residual learning operations alleviate the gradient problems caused by stacking convolutional layers and enhance the ability of feature *** ability further enables the proposed model to acquire effective feature information at a lowcost,which canmake ourmodel keep smallweight *** validation and comparison with other models of COVID-19 pneumonia detection on the well-known COVIDx dataset show that(1)the sensitivity of COVID-19 pneumonia detection is improved from 88.2%(non-COVID-19)and 77.5%(COVID-19)to 95.3%(non-COVID-19)and 96.5%(COVID-19),*** positive predictive value is also respectively increased from72.8%(non-COVID-19)and 89.0%(COVID-19)to 88.8%(non-COVID-19)and 95.1%(COVID-19).(2)Compared with the weight parameters of the COVIDNet-small network,the value of the proposed model is 13 M,which is slightly higher than that(11.37 M)of the COVIDNet-small ***,the corresponding accuracy is improved from 85.2%to 93.0%.The above results illustrate the proposed model can gain an efficient balance between accuracy and weight parameters.

关键词： COVID-19 chest x-ray images multi-class classification convolutional neural network residual learning

A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

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Computers, Materials & Continua 2022年第1期70卷 1381-1399页

作者： Ahmed Reda Sherif Barakat Amira Rezk Department of Information System Faculty of Computer and Information ScienceMansoura UniversityMansouraEgypt

Many respiratory infections around the world have been caused by ***-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival *** is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud *** learning(ML)has been used to examine chest x-ray *** this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest x-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal ***,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature ***,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN *** selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest x-ray *** proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

关键词： Butterfly optimization algorithm(BOA) covid-19 chest x-ray images convolutional neural network(CNN) extreme learning machine(ELM) feature selection

A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

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Intelligent Automation & Soft Computing 2023年第1期35卷 163-180页

作者： Yassir Edrees Almalki Maryam Zaffar Muhammad Irfan Mohammad Ali Abbas Maida Khalid K.S.Quraishi Tariq Ali Fahad Alshehri Sharifa Khalid Alduraibi Abdullah AAsiri Mohammad Abd Alkhalik Basha Alaa Alduraibi M.K.Saeed Saifur Rahman Department of Medicine Division of RadiologyMedical CollegeNajran UniversityNajran11001Saudi Arabia Department of Computer Science and Information Technology Ibadat International UniversityIslamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran11001Saudi Arabia Department of Chemical Engineering PIEAS UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University Islamabad(CUI)Sahiwal Campus57000Pakistan Department of Radiology College of MedicineQassim UniversityBuraydah52571Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran11001Saudi Arabia Faculty of Human Medicine Zagazig University44631Egypt

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the *** x-ray is considered very useful and less time-consuming for monitoring COVID *** doubt,x-ray is considered as a quick screening method,but due to variations in features of images which are of x-rays category with Corona confirmed cases,the domain expert is *** address this issue,we proposed to utilize deep learning *** this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category x-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients *** performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the x-ray *** proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of *** system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 ***,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

关键词： Biomedical systems chest x-ray images CNN COVID-19 swin transformer image processing

chest Radiographs Based Pneumothorax Detection Using Federated Learning

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Computer Systems Science & Engineering 2023年第11期47卷 1775-1791页

作者： Ahmad Almadhor Arfat Ahmad Khan Chitapong Wechtaisong Iqra Yousaf Natalia Kryvinska Usman Tariq Haithem Ben Chikha Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Prince Sattam bin Abdulaziz University AlKharjSaudi Arabia

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest *** most persistent disease,as well as one that necessitates particular patient care and the privacy of their health *** radiologists find it challenging to diagnose pneumothorax due to the variations in *** learning-based techniques are commonly employed to solve image categorization and segmentation ***,it is challenging to employ it in the medical field due to privacy issues and a lack of *** address this issue,a federated learning framework based on an xception neural network model is proposed in this *** pneumothorax medical image dataset is obtained from the Kaggle *** preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s ***-max normalization technique is used to normalize the data,and the features are extracted from chest xray *** dataset converts into two windows to make two clients for local model *** neural network model is trained on the dataset individually and aggregates model updates from two clients on the server *** decrease the over-fitting effect,every client analyses the results three *** 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%*** experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

关键词： Privacy preserving pneumothorax disease federated learning chest x-ray images healthcare machine learning deep learning

Multi-Classification Network for Identifying COVID-19 Cases Using Deep Convolutional Neural Networks

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Journal on Internet of Things 2021年第2期3卷 39-51页

作者： Sajib Sarker Ling Tan Wenjie Ma Shanshan Rong Osibo Benjamin Kwapong Oscar Famous Darteh School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

The novel coronavirus 2019(COVID-19)rapidly spreading around the world and turns into a pandemic situation,consequently,detecting the coronavirus(COVID-19)affected patients are now the most critical task for medical *** deficiency of medical testing kits leading to huge complexity in detecting COVID-19 patients worldwide,resulting in the number of infected cases is ***,a significant study is necessary about detecting COVID-19 patients using an automated diagnosis method,which hinders the spreading of *** this paper,the study suggests a Deep Convolutional Neural Network-based multi-classification framework(COV-MCNet)using eight different pre-trained architectures such as VGG16,VGG19,ResNet50V2,DenseNet201,InceptionV3,MobileNet,InceptionResNetV2,xception which are trained and tested on the x-ray images of COVID-19,Normal,Viral Pneumonia,and Bacterial *** results from 4-class(Normal ***-19 *** Pneumonia *** Pneumonia)demonstrated that the pre-trained model DenseNet201 provides the highest classification performance(accuracy:92.54%,precision:93.05%,recall:92.81%,F1-score:92.83%,specificity:97.47%).Notably,the DenseNet201(4-class classification)pre-trained model in the proposed COV-MCNet framework showed higher accuracy compared to the rest seven *** to mention that the proposed COV-MCNet model showed comparatively higher classification accuracy based on the small number of pre-processed datasets that specifies the designed system can produce superior results when more data become *** proposed multi-classification network(COV-MCNet)significantly speeds up the existing radiology based method which will be helpful for the medical community and clinical specialists to early diagnosis the COVID-19 cases during this pandemic.

关键词： COVID-19 chest x-ray images deep convolutional neural network COV-MCNet deep learning