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Prediction of Covid-19 Based on chest x-ray Images Using Deep Learning with CNN

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Computer Systems Science & Engineering 2022年第6期41卷 1223-1240页

作者： Anika Tahsin Meem Mohammad Monirujjaman Khan Mehedi Masud Sultan Aljahdali Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withchest x-ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using chest x-ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the x-ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest x-ray image can be browsed and fed into the program by medical personnelor the general public.

关键词： Covid-19 prediction covid-19 coronavirus normal deep learning convolutional neural network image processing chest x-ray

Optimal Synergic Deep Learning for COVID-19 Classification Using chest x-ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.chest x-ray(CxR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CxR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CxRs under different severity levels based on CxR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest x-ray COVID-19 optimized synergic deep learning preprocessing public health

COVIDx: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest x-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe.Objective: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest x-ray (CxR) image can effortlessly be used as a substitute modality to diagnose the COVID-19.Method: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDx) that uses deep feature maps of CxR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification algorithms.Results: We evaluated COVIDx through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDx showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this purpose.Conclusions: Our proposed method (COVIDx), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected patients.Availability: We made COVIDx easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest x-ray contagious infection

Radiography Image Classification Using Deep Convolutional Neural Networks

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Journal of Computer and Communications 2024年第6期12卷 199-209页

作者： Ahmad Chowdhury Haiyi Zhang Department of Computer Science Acadia University Wolfville Canada

Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the n

关键词： CNN Radiography Image Classification R Keras chest x-ray Machine Learning

Transfer Learning Approach to Classify the x-ray Image that Corresponds to Corona Disease Using ResNet50 Pre-Trained by ChexNet

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Journal of Intelligent Learning Systems and Applications 2024年第2期16卷 80-90页

作者： Mahyar Bolhassani Electrical and Computer Engineering University of Louisville Louisville KY USA

The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individuals and halt further transmission. x-ray imaging of the lungs is one of the most reliable diagnostic tools. Utilizing deep learning, we can train models to recognize the signs of infection, thus aiding in the identification of COVID-19 cases. For our project, we developed a deep learning model utilizing the ResNet50 architecture, pre-trained with ImageNet and ChexNet datasets. We tackled the challenge of an imbalanced dataset, the CoronaHack chest x-ray dataset provided by Kaggle, through both binary and multi-class classification approaches. Additionally, we evaluated the performance impact of using Focal loss versus Cross-entropy loss in our model.

关键词： x-ray Classification Convolutional Neural Network ResNet Transfer Learning Supervised Learning COVID-19 chest x-ray

chest Radiography: General Practitioners’ Compliance with Recommendations

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Open Journal of Medical Imaging 2024年第2期14卷 56-63页

作者： Milckisédek Judicaël Marouruana Some Aïda Ida Tankoano Pakisba Ali Ouedraogo Bassirou Kindo Nina-Astrid Ouedraogo Mohammed Ali Harchaoui Department of Medical Imaging The Ouahigouya Regional University Hospital University of Ouahigouya Ouahigouya Burkina Faso Department of Medical Imaging Souro Sanou University Hospital Nazi Boni University Bobo Dioulasso Burkina Faso Department of Medical Imaging Bogodogo University Hospital Joseph Ki Zerbo University Ouagadougou Burkina Faso Private Radiologist Lille France

Introduction: chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest x-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. chest x-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest x-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest x-rays. Non-recommended chest x-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.

关键词： chest x-ray Recommendations General Practitioners Prescription

Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

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Data Intelligence 2023年第4期5卷 1008-1032页

作者： Abdullahi Umar Ibrahim Ayse Gunnay Kibarer Fadi Al-Turjman Department of Biomedical Engineering Near East UniversityNicosiaMersin 10Turkey Research Center for Al and loT Faculty of EngineeringUniversity of KyreniaMersin 10Turkey Artificial Intelligence Engineering Dept. Al and Robotics InstituteNear East UniversityMersin 1Turkey

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools.Tuberculosis can be detected from microscopic slides and chest x-ray but as a result of the high cases of tuberculosis,this method can be tedious for both Microbiologists and Radiologists and can lead to miss-diagnosis.These challenges can be solved by employing Computer-Aided Detection(CAD)via Al-driven models which learn features based on convolution and result in an output with high accuracy.In this paper,we described automated discrimination of x-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet Models.The study employed chest x-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle repository.For classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 splits.For classification of tuberculosis using x-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 splits.Our result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

关键词： Tuberculosis Deep Learning Pretrained AlexNet chest x-ray Microscopic slide

Comparison between SARS-CoV-2 positive and negative pneumonia in children:A retrospective analysis at the beginning of the pandemic

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World Journal of Experimental Medicine 2022年第2期12卷 26-35页

作者： Adil Zhamankulov Rafail Rozenson Marina Morenko Ulzhan Akhmetova Alina Tyo Dimitri Poddighe Department of Children's diseases Astana Medical UniversityFirst Children's Municipal HospitalNur-Sultan 010000Kazakhstan Clinical Academic Department of Pediatrics National Research Center for Maternal and Child HealthUniversity Medical CenterNur-Sultan 010000Kazakhstan

BACKGROUND Even though coronavirus 2019 disease(COVID-19)clinical course in children is much milder than in adults,pneumonia can occur in the pediatric population as well.Here,we reported a single-center pediatric case series of COVID-19 from Kazakhstan during the first wave of pandemic.AIM To analyze the main clinical and laboratory aspects in severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)positive and negative children diagnosed with pneumonia.METHODS This is a retrospective analysis of 54 children,who were medically assessed as close contacts of COVID-19 adults in their family setting,between June and September 2020.These children were all hospitalized:We compared the clinical and laboratory characteristics of children affected with pneumonia in the presence(group 1)or absence(group 2)of SARS-CoV-2 infection.RESULTS Overall,the main clinical manifestations at the admission were fever,cough,loss of appetite,fatigue/weakness,nasal congestion and/or rhinorrhea,and dyspnea.Based on the SARS-CoV-2 polymerase chain reaction(PCR)test,24 positive children with pneumonia(group 1)and 20 negative children with pneumonia(group 2)were identified;10 positive children did not show any radiological findings of pneumonia.No significant differences were found between the two pneumonia study groups for any clinical and laboratory parameters,except for C-reactive protein(CRP).Of course,both pneumonia groups showed increased CRP values;however,the COVID-19 pneumonia group 1 showed a significantly higher increase of CRP compared to group 2.CONCLUSION In our case series of children assessed for SARS-CoV-2 infection based on contact tracing,the acute inflammatory response and,in detail,CRP increase resulted to be more pronounced in COVID-19 children with pneumonia than in children with SARS-CoV-2-unrelated pneumonia.However,because of multiple limitations of this study,larger,controlled and more complete clinical studies are needed to verify this finding.

关键词： Pediatric COVID-19 SARS-CoV-2 Pneumonia C-reactive protein chest x-ray Inflammatory parameters

chest radiological finding of COVID-19 in patients with and without diabetes mellitus:Differences in imaging finding

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World Journal of Radiology 2022年第1期14卷 13-18页

作者： Sunay Gangadharan Storm Parker Fahad Wali Ahmed Department of Radiology University Hospitals Sussex NHS Foundation TrustBrighton BN25BEUnited Kingdom Department of Medical Oncology King Faisal Specialist Hospital and Research CentreMadinah 42522Saudi Arabia

The pandemic of novel coronavirus disease 2019(COVID-19)is caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Diabetes mellitus is a risk factor for developing severe illness and a leading cause of death in patients with COVID-19.Diabetes can precipitate hyperglycaemic emergencies and cause prolonged hospital admissions.Insulin resistance is thought to cause endothelial dysfunction,alveolar capillary micro-angiopathy and interstitial lung fibrosis through pro-inflammatory pathways.Autopsy studies have also demonstrated the presence of microvascular thrombi in affected sections of lung,which may be associated with diabetes.chest imaging using x-ray(CxR)and computed tomography(CT)of chest is used to diagnose,assess disease progression and severity in COVID-19.This article reviews current literature regarding chest imaging findings in patients with diabetes affected by COVID-19.A literature search was performed on PubMed.Patients with diabetes infected with SARSCoV-2 are likely to have more severe infective changes on CxR and CT chest imaging.Severity of airspace consolidation on CxR is associated with higher mortality,particularly in the presence of co-morbidities such as ischaemic heart disease.Poorly controlled diabetes is associated with more severe acute lung injury on CT.However,no association has been identified between poorlycontrolled diabetes and the incidence of pulmonary thromboembolism in patients with COVID-19.

关键词： Diabetes mellitus COVID-19 chest x-ray chest imaging using x-ray Computed tomography of chest