☆ 4.7 Article

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans

COMPUTERS IN BIOLOGY AND MEDICINE (2023)

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 Biology

Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images

Qianqian Qi et al.

Summary: This study proposed a fully automatic deep learning pipeline for rapid and accurate differentiation of COVID-19 from CAP using CT images. The results showed excellent performance of the pipeline in lung segmentation, selection of slices with lesions, and slice-level prediction, achieving a 100% prediction accuracy at the patient level for both datasets.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images

R. T. Subhalakshmi et al.

Summary: This study aims to develop a deep learning-based model for COVID-19 diagnosis and classification from CT images, showing superior performance in experimental validation with high accuracy and sensitivity.

CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

COVID-WideNet-A capsule network for COVID-19 detection

P. K. Gupta et al.

Summary: COVID-WideNet, a capsule network, is proposed for diagnosing COVID-19 cases using Chest X-ray images. It achieves high diagnostic accuracy and outperforms other CNN-based approaches, with a significantly lower number of trainable parameters.

APPLIED SOFT COMPUTING (2022)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Automated lung vessel segmentation reveals blood vessel volume redistribution in viral pneumonia

Julien Poletti et al.

Summary: The purpose of this study was to develop a robust method for automated lung vessel segmentation and morphology analysis and apply it to a large chest CT dataset. The results showed that in COVID-19 and Influenza, the blood vessel volume is redistributed from small to large vessels in the lung. This study contributes to a better understanding of vascular changes in infectious lung diseases.

EUROPEAN JOURNAL OF RADIOLOGY (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Effective hybrid deep learning model for COVID-19 patterns identification using CT images

Dheyaa Ahmed Ibrahim et al.

Summary: This study investigates the use of hybrid deep learning methods for the quick and accurate identification of individuals infected with COVID-19 based on their lung CT images. A reliable COVID-19 prediction network is proposed, starting with lung CT scan image segmentation and ending with disease prediction. The system combines pre-trained models to extract affective features and achieved a 95% accuracy rate on a publicly available dataset. The developed model shows effectiveness in accurately screening COVID-19 CT images and could serve as an additional diagnostic tool for clinical professionals.

EXPERT SYSTEMS (2022)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

MHA-CoroCapsule: Multi-Head Attention Routing-Based Capsule Network for COVID-19 Chest X-Ray Image Classification

Fudong Li et al.

Summary: The outbreak of COVID-19 has posed great challenges to global healthcare systems, particularly in terms of efficient detection methods. This study proposes a capsule network model with a multi-head attention routing algorithm for fast and accurate diagnostics of COVID-19 diseases from chest X-ray images.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

添加到收藏夹

Article Virology

Using artificial intelligence to improve the diagnostic efficiency of pulmonologists in differentiating COVID-19 pneumonia from community-acquired pneumonia

Erdal In et al.

Summary: This study utilizes a deep learning-based AI model to analyze the contribution of AI in distinguishing COVID-19 pneumonia from CAP using CT scans. The results show that with the assistance of AI, pulmonologists demonstrate significantly improved accuracy, sensitivity, and specificity in diagnosing COVID-19.

JOURNAL OF MEDICAL VIROLOGY (2022)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study

Fangyi Xu et al.

Summary: The study aims to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. The results show that the three-dimensional algorithm 3DMTM achieves excellent performance in COVID-19 discrimination.

MEDICAL PHYSICS (2022)

添加到收藏夹

Article Health Care Sciences & Services

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Karrar Hameed Abdulkareem et al.

Summary: COVID-19 is a global health issue with high fatality rate. This study developed a diagnostic system for COVID-19 using deep learning techniques, specifically convolutional neural networks, stacked autoencoders, and deep neural networks. The system achieved high accuracy in classifying CT images and outperformed existing state-of-the-art models in detecting the virus.

JOURNAL OF HEALTHCARE ENGINEERING (2022)

添加到收藏夹

Article Health Care Sciences & Services

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model

Sania Shamim et al.

Summary: The COVID-19 pandemic has had a devastating impact globally, and this research proposes a more accurate and reliable segmentation approach for identifying ground glass opacity in COVID-19 CT images.

JOURNAL OF HEALTHCARE ENGINEERING (2022)

添加到收藏夹

Article Mathematical & Computational Biology

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

Mazin Abed Mohammed et al.

Summary: This study proposes an integrated method for selecting the optimal deep learning model for COVID-19 diagnosis using a novel crow swarm optimization algorithm. The algorithm evaluates the performance of different models and selects the best coefficients distribution to determine the optimal model.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models

Ali Tariq Nagi et al.

Summary: The modern scientific world is actively fighting against newly emerging pandemics, including COVID-19. This research highlights the use of artificial intelligence for more accurate diagnosis of COVID-19 compared to traditional methods. Various deep learning models were evaluated using a large dataset, and the Xception model showed the highest precision and accuracy.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Article Mathematics

An Optimized Decision Support Model for COVID-19 Diagnostics Based on Complex Fuzzy Hypersoft Mapping

Muhammad Saeed et al.

Summary: This article proposes a mathematical framework based on a complex fuzzy hypersoft set to tackle the uncertainty and ambiguity in the diagnosis and treatment of COVID-19. The framework expands the range of membership function values and categorizes attributes for accurate disease identification and medication selection. Additionally, it can predict the recovery time for patients.

MATHEMATICS (2022)

添加到收藏夹

Review Computer Science, Artificial Intelligence

Capsule networks for image classification: A review

S. J. Pawan et al.

Summary: Computer vision has undergone a revolution in recent years, transitioning from human-engineered features to automated features. Capsule networks have emerged as a viable alternative to convolutional neural networks, but their adoption is hindered by a lack of information about their conception and workings.

NEUROCOMPUTING (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging

Ramzi Mahmoudi et al.

Summary: This study developed a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. A pre-processing method and U-net architecture were used for image segmentation, and a CNN architecture was used for classification and volume reconstruction to achieve accurate diagnosis.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Review Microbiology

Similarities and Dissimilarities of COVID-19 and Other Coronavirus Diseases

To Sing Fung et al.

Summary: Three deadly zoonotic coronaviruses, including SARS-CoV, MERS-CoV, and SARS-CoV-2, have emerged in humans in less than two decades, leading to severe respiratory diseases. The COVID-19 pandemic has caused a crisis in global health care and socio-economic development, highlighting the constantly evolving nature of coronaviruses. Understanding the pathogenic mechanisms and host immune responses is crucial in developing effective strategies for prevention and treatment.

ANNUAL REVIEW OF MICROBIOLOGY, VOL 75, 2021 (2021)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images

Shouliang Qi et al.

Summary: The study proposed a DR-MIL method for distinguishing COVID-19 from CAP, achieving an accuracy of 95% which outperformed other methods. Significant differences were observed in the deep features and spatial pattern of lesions between COVID-19 and CAP. DR-MIL effectively assists in accurately identifying COVID-19 in CT images, providing valuable insight for medical professionals.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi -task contrastive learning for automatic CT and X-ray diagnosis of COVID-19

Jinpeng Li et al.

Summary: The contrastive multi-task convolutional neural network (CMT-CNN) improves the generalization of automatic COVID-19 diagnosis using contrastive learning, achieving diagnosis of COVID-19 and local aggregation through the main and auxiliary tasks. Experimental results demonstrate significant accuracy improvement on CT and X-ray datasets, showcasing the effectiveness of CMT-CNN.

PATTERN RECOGNITION (2021)

添加到收藏夹

Article Engineering, Biomedical

LungSeg-Net: Lung field segmentation using generative adversarial network

Swati P. Pawar et al.

Summary: An approach based on generative adversarial networks, called LungSeg-Net, is proposed to improve the accuracy of lung segmentation. The network extracts multi-scale features using a multi-scale dense-feature extraction module, and utilizes decoders to obtain the lung segmentation map, learning relevant features of dense abnormalities and remaining invariant to abnormality size.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans

Mohamed Abdel-Basset et al.

Summary: A novel two-stage deep learning framework is proposed to differentiate COVID-19 from community-acquired pneumonia by identifying the infection region in CT scans. The model utilizes a U-shaped network for lung area segmentation, applies transfer learning for feature extraction, captures multi-scale information and attention mechanism for powerful classification, and proposes an infection prediction module for interpretable classification decision. The model outperforms cutting-edge studies in segmentation and classification performance on public datasets.

PATTERN RECOGNITION LETTERS (2021)

添加到收藏夹

Letter Critical Care Medicine

Should Vascular Abnormalities Be Integrated Into the Chest CT Imaging Signature of Coronavirus Disease 2019?

David C. Rotzinger et al.

CHEST (2021)

添加到收藏夹

Article Medicine, General & Internal

Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Yazan Qiblawey et al.

Summary: The study proposed a cascaded system to detect and quantify COVID-19 infections from CT images, achieving high sensitivity and specificity, precise localization of infections, and the ability to discriminate between different severity levels of infection. The system showed excellent performance in experiments and could accurately localize infections of various shapes and sizes, even in small infection regions rarely considered in recent studies.

DIAGNOSTICS (2021)

添加到收藏夹

Article Biology

DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network

Hao Quan et al.

Summary: A new deep learning framework DenseCapsNet is proposed, which integrates DenseNet and CapsNet to achieve high accuracy and sensitivity detection of novel coronavirus pneumonia CXR images.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study

Ali Abbasian Ardakani et al.

Summary: This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. The system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model.

PATTERN RECOGNITION LETTERS (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Three-stage segmentation of lung region from CT images using deep neural networks

Michael Osadebey et al.

Summary: This research aims to improve the diagnostic accuracy of respiratory diseases by implementing each of the three stages of traditional methods using deep learning methods on five different configurations of training data. By utilizing ground truths from 3DIRCAD and ILD databases, along with data augmentation from LIDC-IDRI image collection and a realistic phantom, the proposed method achieved Dice-scores ranging from 0.76 to 0.95. The study emphasizes the importance of efficient and clinically relevant automated segmentation methods for the diagnosis of respiratory diseases, highlighting the potential benefits of deep learning models in improving segmentation accuracy and clinical relevance.

BMC MEDICAL IMAGING (2021)

添加到收藏夹

Article Health Care Sciences & Services

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Tahereh Javaheri et al.

Summary: The article discusses the importance of early and accurate diagnosis of Covid-19, as well as the pros and cons of RT-PCR and CT imaging. A new open-source framework called CovidCTNet is introduced to improve the accuracy of CT imaging detection of Covid-19 to 95%.

NPJ DIGITAL MEDICINE (2021)

添加到收藏夹

Article Infectious Diseases

Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR

Victor M. Corman et al.

EUROSURVEILLANCE (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Sunyi Zheng et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)

Michael Chung et al.

RADIOLOGY (2020)

添加到收藏夹

Article Biochemistry & Molecular Biology

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

Kang Zhang et al.

CELL (2020)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review

Zheng Ye et al.

EUROPEAN RADIOLOGY (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing

Bohan Jia et al.

APPLIED SCIENCES-BASEL (2020)

添加到收藏夹

Article Infectious Diseases

CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China

Heng Meng et al.

JOURNAL OF INFECTION (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy

Lin Li et al.

RADIOLOGY (2020)

添加到收藏夹

Letter Medicine, General & Internal

COVID-19: consider cytokine storm syndromes and immunosuppression

Puja Mehta et al.

LANCET (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of Small Pulmonary Blood Vessels in COVID-19 Patients Using HRCT

Muriel Lins et al.

ACADEMIC RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT

Jiangdian Song et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia

Xi Ouyang et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

UNet plus plus : Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

Zongwei Zhou et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)

添加到收藏夹

Article Instruments & Instrumentation

Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Md Mamunur Rahaman et al.

JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY (2020)

添加到收藏夹

Article Computer Science, Information Systems

Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction

Huidong Xie et al.

IEEE ACCESS (2020)

添加到收藏夹

Article Computer Science, Information Systems

RS-CapsNet: An Advanced Capsule Network

Shuai Yang et al.

IEEE ACCESS (2020)

添加到收藏夹

Article Computer Science, Information Systems

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

Abdul Waheed et al.

IEEE ACCESS (2020)

添加到收藏夹

Article Computer Science, Information Systems

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images

Shaoping Hu et al.

IEEE ACCESS (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Recurrent residual U-Net for medical image segmentation

Md Zahangir Alom et al.

JOURNAL OF MEDICAL IMAGING (2019)

添加到收藏夹

Article Computer Science, Information Systems

Ensemble Learners of Multiple Deep CNNs for Pulmonary Nodules Classification Using CT Images

Baihua Zhang et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Computer Science, Information Systems

Detection and Classification of Pulmonary Nodules Using Convolutional Neural Networks: A Survey

Patrice Monkam et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Kenneth Clark et al.

JOURNAL OF DIGITAL IMAGING (2013)

添加到收藏夹

Article Ophthalmology

ImageNet: Constructing a large-scale image database

L. Fei-Fei et al.

JOURNAL OF VISION (2010)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Efficacy of Computer-Aided Detection System and Thin-Slab Maximum Intensity Projection Technique in the Detection of Pulmonary Nodules in Patients With Resected Metastases

Eun-Ah Park et al.

INVESTIGATIVE RADIOLOGY (2009)

添加到收藏夹

Review Medicine, General & Internal

Current concepts - Computed tomography - An increasing source of radiation exposure

David J. Brenner et al.

NEW ENGLAND JOURNAL OF MEDICINE (2007)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.