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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 *** x-ray(CxR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** 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 *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CxRs under different severity levels based on CxR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** 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 ***: 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 ***: 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 ***: 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 ***: 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 ***: 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

Deep learning models for tuberculosis detection and infected region visualization in chest x-ray images

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Intelligent Medicine 2024年第2期4卷 104-113页

作者： Vinayak Sharma Nillmani Sachin Kumar Gupta Kaushal Kumar Shukla School of Electronics and Communication Engineering Shri Mata Vaishno Devi University Jammu and KashmirIndia School of Biomedical Engineering Indian Institute of TechnologyBanaras Hindu UniversityVaranasiIndia Department of Computer Science and Engineering Indian Institute of TechnologyBanaras Hindu UniversityVaranasiIndia

Objective Tuberculosis(TB)is among the most frequent causes of infectious-disease-related *** being treatable by antibiotics,tuberculosis often goes misdiagnosed and untreated,especially in rural and low-resource *** x-rays are frequently used to aid diagnosis;however,this presents additional challenges because of the possibility of abnormal radiological appearance and a lack of radiologists in areas where the infection is most *** deep-learning-based imaging techniques for computer-aided diagnosis has the potential to enable accurate diagnoses and lessen the burden on medical *** the present work,we aimed to develop deep-learning-based segmentation and classification models for accurate and precise detection of tuberculosis in chest x-ray images,with visualization of infection using gradient-weighted class activation mapping(Grad-CAM)*** First,we trained the UNet segmentation model using 704 chest x-ray radiographs taken from the Montgomery County and Shenzhen Hospital ***,we implemented the trained UNet model on 1,400 tuberculosis and control chest x-ray scans to segment the lung *** images were taken from the National Institute of Allergy and Infectious Diseases(NIAID)TB portal program ***,we applied the deep learning xception model to classify the segmented lung region into tuberculosis and normal *** further investigated the visualization capabilities of the model using Grad-CAM to view tuberculosis abnormalities in chest x-rays and discuss them from radiological *** For segmentation by the UNet model,we achieved accuracy,Jaccard index,Dice coefficient,and area under the curve(AUC)values of 96.35%,90.38%,94.88%,and 0.99,*** classification by the xception model,we achieved classification accuracy,precision,recall,F1-score,and AUC values of 99.29%,99.30%,99.29%,99.29%,and 0.999,*** Grad-CAM heatmap images from the tuberculos

关键词： Tuberculosis Artificial intelligence Deep learning Segmentation Classification chest x-ray

An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

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Computers, Materials & Continua 2021年第11期69卷 2475-2491页

作者： Saddam Bekhet Monagi H.Alkinani Reinel Tabares-Soto M.Hassaballah Faculty of Commerce South Valley UniversityQenaEgypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Electronics and Automation Universidad Autónoma de ManizalesManizales170001Colombia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and *** situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest x-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily *** hospitals possess x-ray or CT imaging equipments that can be used for early detection of *** by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest x-ray or CT ***,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several *** paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN *** proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are *** outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.

关键词： Artificial intelligence COVID-19 chest CT chest x-ray deep learning

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 *** 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 *** 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 *** this paper,we described automated discrimination of x-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet *** study employed chest x-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle *** classification of tuberculosis using microscopic slide images,the model achieved 90.56%accuracy,97.78%sensitivity and 83.33%specificity for 70:30 *** classification of tuberculosis using x-ray images,the model achieved 93.89%accuracy,96.67%sensitivity and 91.11%specificity for 70:30 *** 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

Active case-finding for tuberculosis by mobile teams in Myanmar:yield and treatment outcomes

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Infectious Diseases of Poverty 2017年第1期6卷 683-690页

作者： Ohnmar Myint Saw Saw Petros Isaakidis Mohammed Khogali Anthony Reid Nguyen Binh Hoa Thi Thi Kyaw Ko Ko Zaw Tin Mi Mi Khaing Si Thu Aung National Tuberculosis Programme Corner of Mingyi Road and Minglar StreetPathein townshipAyeyarwaddy RegionMyanmar Department of Medical Research YangonMyanmar Medecins sans Frontieres Operational Research UnitLuxembourgLuxembourg National Tuberculosis Programme HanoiVietnam Centre for Operational Research International Union Against Tuberculosis and Lung DiseaseParisFrance TB Unit WHO Country Office for MyanmarYangonMyanmar

Background:Since 2005,the Myanmar National Tuberculosis Programme(NTP)has been implementing active case finding(ACF)activities involving mobile teams in hard-to-reach *** study revealed the contribution of mobile team activities to total tuberculosis(TB)case detection,characteristics of TB patients detected by mobile teams and their treatment ***:This was a descriptive study using routine programme data between October 2014 and December *** team activities were a one-stop service and included portable digital chest radiography(CxR)and microscopy of two sputum *** algorithm of the case detection included screening patients by symptoms,then by CxR followed by sputum microscopy for *** patients were started on treatment and followed until a final outcome was ***:A total of 9349 people with symptoms suggestive of TB were screened by CxR,with an uptake of 96.6%.Of those who were meant to undergo sputum smear microscopy,51.4%had sputum ***,504 TB patients were identified by the mobile teams and the overall contribution to total TB case detection in the respective townships was 25.3%.Among total cases examined by microscopy,6.4%were sputum smear positive *** success rate was high as 91.8%in study townships compared to national rate 85%(2014 cohort).Conclusions:This study confirmed the feasibility and acceptability of ACF by mobile teams in hard-to-reach contexts,especially when equipped with portable,digital CxR machines that provided immediate ***,the follow-up process of sputum examination created a significant barrier to confirmation of the *** order to optimize the ACF through mobile team activity,future ACF activities were needed to be strengthened one stop service including molecular diagnostics or provision of sputum cups to all presumptive TB cases prior to CxR and testing if CxR suggestive of TB.

关键词： Mobile team Active case finding chest x-ray Treatment outcomes

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 ***,we reported a single-center pediatric case series of COVID-19 from Kazakhstan during the first wave of *** To analyze the main clinical and laboratory aspects in severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)positive and negative children diagnosed with *** 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 *** 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 *** Overall,the main clinical manifestations at the admission were fever,cough,loss of appetite,fatigue/weakness,nasal congestion and/or rhinorrhea,and *** 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 *** 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 *** 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 ***,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

Radiological features of lung changes caused by avian influenza subtype A H5N1 virus： report of two severe adult cases with regular follow-up

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Chinese Medical Journal 2010年第1期123卷 100-104页

作者： LU Pu-xuan Yi-xiang Wang ZHOU Bo-ping GE Yang ZHU Wen-Ke CHEN xin-chun RAN xian-gui Department of Radiology Department of Diagnostic Radiology and Organ Imaging Prince ofWales Hospital The Chinese University of Hong Kong ShatinNT Hong Kong China Department ofInfectious Diseases Second Municipal Hospital of Fuyang Fuyang Anhui 236015China

Several subtypes of avian influenza A have been shown to cross the species barrier and infect humans, leadingto human cases of avian influenza） Till June 2, 2009, globally there were 433 confirmed human cases of avian influenza caused by H5N1 virus, with a death rate of 60.5%.3 This is far higher than the reported 11% death rate of severe acute respiratory syndrome （SARS）.4 The epidemiologic features of human case of influenza A subtype H5N1 virus infection consist of high incidence rate in cold weather, high susceptibility in population of younger age associated with rapid onset of the disease and devastating illness in humans.12＇5＇6 H5N1 virus is mostly transmitted to humans directly through contact with infected birds or their secretions and the patients present with an influenza type illness with fever, cough, sore throat, malaise, and gastrointestinal symptoms; which can result in a rapidly progressive primary viral pneumonia and respiratory failure.2 Patients above the age of 5 years are likely to have an adverse course of disease.5

关键词： chest x-ray tomography, computed infectious diseases lung diseases avian-flu