相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Computer Science, Software Engineering
Ferhat Bozkurt
Summary: Automated early diagnosis of COVID-19 using computer-aided tools is crucial, as common features in radiology images of COVID-19 and other lung diseases make detection challenging. Classifying chest x-ray images of non-COVID-19 and COVID-19 cases can help streamline the process.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Software Engineering
Shubham Mahajan et al.
Summary: The outbreak of the novel coronavirus has had a significant impact globally, affecting economies, structures, and education in various nations. However, advancements in data analytics and machine learning have led to improvements in diagnostic tools, reducing the growth rate of affected patients.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Artificial Intelligence
M. Adimoolam et al.
Summary: The various stages of COVID-19 can be effectively predicted and classified using X-ray imaging. Employing a hybrid method and utilizing diverse datasets enhances prediction accuracy and data processing capabilities.
Article
Computer Science, Information Systems
Shashwat Sanket et al.
Summary: This paper introduces a Convolutional Neural Network (CNN) based model, CovCNN, for COVID-19 detection using chest X-ray images, aiming to expedite the diagnostic process under high workload conditions. By incorporating multiple folds of CNN and depth wise convolution, the model efficiently extracts diversified features from X-rays, achieving a classification accuracy of 98.4%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Usman Muhammad et al.
Summary: COVID-19 is a rapidly spreading viral disease and the test of choice for diagnosis is reverse transcription-polymerase chain reaction (RT-PCR). X-ray and CT can be used as substitutes for countries where PCR is not readily available. Machine learning methods, specifically deep learning with convolutional neural networks (CNNs), have shown promise for detecting COVID-19 infection from X-ray images.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Asmaa Abbas et al.
Summary: This paper validates a deep CNN model called DeTraC, which utilizes a class decomposition mechanism to handle irregularities in medical image datasets. Experimental results demonstrate the high accuracy of DeTraC in detecting COVID-19 X-ray images.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Rachna Jain et al.
Summary: Covid-19 is a rapidly spreading viral disease that affects both humans and animals. Deep learning techniques can provide useful analysis of chest x-ray images to aid in the screening of Covid-19. The Xception model shows the highest accuracy in detecting chest x-ray images compared to other models.
APPLIED INTELLIGENCE
(2021)
Article
Ritika Nandi et al.
Research on Biomedical Engineering
(2021)
Article
Computer Science, Artificial Intelligence
Daniel Iglesias Moris et al.
Summary: The current COVID-19 pandemic has caused more than 100 million cases and over two million deaths worldwide, urging the need for rapid and accurate diagnostic methods. Utilizing chest X-ray imaging can explore pathological structures, with portable devices being recommended over conventional fixed machinery. The subjectivity and fatigue of clinicians pose challenges in diagnosis, but computer-aided methodologies can enhance accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Medicine, General & Internal
Mohamed Elgendi et al.
Summary: Chest X-ray imaging technology is crucial for early detection of COVID-19, and deep learning methods can improve accuracy. Data augmentation helps reduce overfitting and enhance predictive accuracy on testing datasets.
FRONTIERS IN MEDICINE
(2021)
Article
Engineering, Biomedical
Emine Ucar et al.
Summary: The study proposes a deep learning approach for rapid and accurate detection of Covid-19 on X-ray images, extracting deep features using pre-trained architectures and employing a two-stage classifier method for binary classification. The Bi-LSTM network showed superior performance with 92.489% accuracy compared to other classifiers, including well-known ensemble approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Biology
Mohammad Momeny et al.
Summary: This study introduces a data augmentation strategy by determining the type and value of noise density to improve the robustness and generalization of deep convolutional neural networks for COVID-19 detection. By utilizing learning-to-augment and denoised X-ray images approaches, new training data is generated and fine-tuned in various networks, leading to superior results compared to existing methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Daphna Keidar et al.
Summary: During the COVID-19 outbreak, chest X-ray imaging has been important in diagnosing and monitoring patients. A deep learning model was proposed for COVID-19 detection from CXRs, along with a tool for retrieving similar patients. The model achieved high accuracy, specificity, and sensitivity on a test dataset.
EUROPEAN RADIOLOGY
(2021)
Article
Health Care Sciences & Services
Karim Hammoudi et al.
Summary: COVID-19, initially presenting flu-like symptoms and causing pneumonia, has led to a global pandemic. This study explores the use of deep learning to automatically analyze chest X-ray images for screening and diagnosing COVID-19 patients.
JOURNAL OF MEDICAL SYSTEMS
(2021)
Article
Computer Science, Information Systems
Sarra Guefrechi et al.
Summary: This study designed a deep learning system for extracting features and detecting COVID-19 from chest X-ray images, and fine-tuned three powerful neural networks on an enhanced dataset through transfer learning, achieving efficient and accurate COVID-19 detection methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Maayan Frid-Adar et al.
Summary: This study estimates the severity of pneumonia in COVID-19 patients through a deep learning model that detects and localizes pneumonia in chest Xray images. By calculating a Pneumonia Ratio based on localization maps, disease severity is assessed to build a temporal disease extent profile for hospitalized patients. The model's applicability to patient monitoring is validated by comparing disease progression profiles generated from synthetic Xrays to those from CT scans.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Rekha Rajagopal
Summary: The new type of coronavirus, SARS-CoV-2, has led to the pandemic of COVID-19 disease, for which there is currently no medication for prevention or cure. A proposed study suggests the potential use of X-ray images to classify individuals as healthy, COVID-19 affected, or Pneumonia affected. The research demonstrates that the SVM model combined with CNN extracted features achieved the highest precision, recall, F1-score, and accuracy in identifying healthy individuals, those with Pneumonia, and those infected with COVID-19.
PATTERN RECOGNITION AND IMAGE ANALYSIS
(2021)
Article
Medical Informatics
Tuan D. Pham
Summary: This study investigated the fine tuning of pretrained convolutional neural networks for COVID-19 classification using chest X-rays. Three pretrained CNNs achieved high classification results without data augmentation, with AlexNet, GoogleNet, and SqueezeNet requiring the least training time among pretrained DL models. These findings contribute to the urgent need for deploying AI tools in the public domain for rapid implementation during the pandemic.
HEALTH INFORMATION SCIENCE AND SYSTEMS
(2021)
Article
Engineering, Biomedical
Ioannis D. Apostolopoulos et al.
JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING
(2020)
Article
Computer Science, Interdisciplinary Applications
Rodolfo M. Pereira et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2020)
Article
Instruments & Instrumentation
Md Mamunur Rahaman et al.
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY
(2020)
Article
Computer Science, Interdisciplinary Applications
Asif Iqbal Khan et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2020)
Article
Computer Science, Information Systems
Michael J. Horry et al.
