Cargando…

Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs

Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially en...

Descripción completa

Detalles Bibliográficos
Autores principales:	Kikkisetti, Shreeja, Zhu, Jocelyn, Shen, Beiyi, Li, Haifang, Duong, Tim Q.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	PeerJ Inc. 2020
Materias:	Bioinformatics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649013/ https://www.ncbi.nlm.nih.gov/pubmed/33194447 http://dx.doi.org/10.7717/peerj.10309

Ejemplares similares

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs
por: Zhu, Jocelyn, et al.
Publicado: (2020)

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection
por: Hussain, Lal, et al.
Publicado: (2020)

Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
por: Li, Xiaoran, et al.
Publicado: (2020)

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images
por: M. Bahgat, Waleed, et al.
Publicado: (2021)

Prioritizing bona fide bacterial small RNAs with machine learning classifiers
por: Eppenhof, Erik J.J., et al.
Publicado: (2019)

TOXIFY: a deep learning approach to classify animal venom proteins
por: Cole, T. Jeffrey, et al.
Publicado: (2019)

Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images
por: Albashish, Dheeb
Publicado: (2022)

Deep learning prediction of mild cognitive impairment conversion to Alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI
por: Ocasio, Ethan, et al.
Publicado: (2021)

A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data
por: Tabares-Soto, Reinel, et al.
Publicado: (2020)

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes
por: Orozco-Arias, Simon, et al.
Publicado: (2021)

Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning
por: Cohen, Joseph Paul, et al.
Publicado: (2020)

Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile
por: Chen, Yuxing, et al.
Publicado: (2021)

DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers
por: Suleman, Muhammad Taseer, et al.
Publicado: (2022)

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers
por: Yu, Xin, et al.
Publicado: (2021)

Deep convolutional neural networks using an active learning strategy for cervical cancer screening and diagnosis
por: Li, Xueguang, et al.
Publicado: (2023)

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints
por: Paggi, Joseph M., et al.
Publicado: (2018)

Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?
por: Johnson, Nathan T., et al.
Publicado: (2018)

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples
por: Bose, Shilpi, et al.
Publicado: (2021)

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation
por: Clark, Alex M., et al.
Publicado: (2014)

Classification of Curcuma longa and Curcuma zanthorrhiza using transfer learning
por: Pratondo, Agus, et al.
Publicado: (2022)

Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs
por: Huhtanen, Jarno T., et al.
Publicado: (2022)

The classification of EEG-based winking signals: a transfer learning and random forest pipeline
por: Mahendra Kumar, Jothi Letchumy, et al.
Publicado: (2021)

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset
por: Fayemiwo, Michael Adebisi, et al.
Publicado: (2021)

Compi: a framework for portable and reproducible pipelines
por: López-Fernández, Hugo, et al.
Publicado: (2021)

Protein function prediction with gene ontology: from traditional to deep learning models
por: Vu, Thi Thuy Duong, et al.
Publicado: (2021)

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework
por: Baghdadi, Nadiah A., et al.
Publicado: (2022)

An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer
por: Jakkaladiki, Sudha Prathyusha, et al.
Publicado: (2023)

Classification of melanonychia, Beau’s lines, and nail clubbing based on nail images and transfer learning techniques
por: Coşar Soğukkuyu, Derya Yeliz, et al.
Publicado: (2023)

Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study
por: Lu, Joyce Q., et al.
Publicado: (2021)

imGLAD: accurate detection and quantification of target organisms in metagenomes
por: Castro, Juan C., et al.
Publicado: (2018)

Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
por: Kensert, Alexander, et al.
Publicado: (2019)

Splicing predictions reliably classify different types of alternative splicing
por: Busch, Anke, et al.
Publicado: (2015)

Comparison of machine learning and deep learning techniques in promoter prediction across diverse species
por: Bhandari, Nikita, et al.
Publicado: (2021)

Benchmarking metagenomics classifiers on ancient viral DNA: a simulation study
por: Arizmendi Cárdenas, Yami Ommar, et al.
Publicado: (2022)

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers
por: Player, Robert A., et al.
Publicado: (2023)

Predicting the effect of variants on splicing using Convolutional Neural Networks
por: Thanapattheerakul, Thanyathorn, et al.
Publicado: (2020)

sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data
por: Kuzniar, Arnold, et al.
Publicado: (2020)

Solving musculoskeletal biomechanics with machine learning
por: Smirnov, Yaroslav, et al.
Publicado: (2021)

Genomics enters the deep learning era
por: Routhier, Etienne, et al.
Publicado: (2022)

Machine learning enhanced cell tracking
por: Soelistyo, Christopher J., et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_uu1d0fhes5n4mv41eircur5qjd