A bi-stage feature selection approach for COVID-19 prediction using chest CT images
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A bi-stage feature selection approach for COVID-19 prediction using chest CT images
Authors
Keywords
-
Journal
APPLIED INTELLIGENCE
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-04-19
DOI
10.1007/s10489-021-02292-8
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management
- (2020) Yan Li et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- Chest CT Findings in Patients with Corona Virus Disease 2019 and its Relationship with Clinical Features
- (2020) Jiong Wu et al. INVESTIGATIVE RADIOLOGY
- Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR
- (2020) Yicheng Fang et al. RADIOLOGY
- CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
- (2020) Michael Chung et al. RADIOLOGY
- Optimization Method for Forecasting Confirmed Cases of COVID-19 in China
- (2020) Mohammed A. A. Al-qaness et al. Journal of Clinical Medicine
- Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease
- (2020) Mingzhi Li et al. ACADEMIC RADIOLOGY
- Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?
- (2020) Chunqin Long et al. EUROPEAN JOURNAL OF RADIOLOGY
- World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)
- (2020) Catrin Sohrabi et al. International Journal of Surgery
- A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19
- (2020) Youssoufa Mohamadou et al. APPLIED INTELLIGENCE
- Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
- (2020) Dilbag Singh et al. EUROPEAN JOURNAL OF CLINICAL MICROBIOLOGY & INFECTIOUS DISEASES
- Marine Predators Algorithm: A nature-inspired metaheuristic
- (2020) Afshin Faramarzi et al. EXPERT SYSTEMS WITH APPLICATIONS
- Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning
- (2020) Aayush Jaiswal et al. JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
- Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach
- (2020) Arnab Kumar Mishra et al. Journal of Healthcare Engineering
- A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
- (2020) Xiaowei Xu et al. Engineering
- OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19
- (2020) Tripti Goel et al. APPLIED INTELLIGENCE
- Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
- (2020) Asmaa Abbas et al. APPLIED INTELLIGENCE
- An improved Dragonfly Algorithm for feature selection
- (2020) Abdelaziz I. Hammouri et al. KNOWLEDGE-BASED SYSTEMS
- A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier
- (2020) Warda M. Shaban et al. KNOWLEDGE-BASED SYSTEMS
- A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images
- (2020) Mohamed Loey et al. NEURAL COMPUTING & APPLICATIONS
- COVID-19 image classification using deep features and fractional-order marine predators algorithm
- (2020) Ahmed T. Sahlol et al. Scientific Reports
- A light CNN for detecting COVID-19 from CT scans of the chest
- (2020) Matteo Polsinelli et al. PATTERN RECOGNITION LETTERS
- Binary dragonfly optimization for feature selection using time-varying transfer functions
- (2018) Majdi Mafarja et al. KNOWLEDGE-BASED SYSTEMS
- Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems
- (2015) Seyedali Mirjalili NEURAL COMPUTING & APPLICATIONS
- Grey Wolf Optimizer
- (2014) Seyedali Mirjalili et al. ADVANCES IN ENGINEERING SOFTWARE
- Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection
- (2013) Zechao Li et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- A review of feature selection methods based on mutual information
- (2013) Jorge R. Vergara et al. NEURAL COMPUTING & APPLICATIONS
- Binary bat algorithm
- (2013) Seyedali Mirjalili et al. NEURAL COMPUTING & APPLICATIONS
- A new local search based hybrid genetic algorithm for feature selection
- (2011) Md. Monirul Kabir et al. NEUROCOMPUTING
- BGSA: binary gravitational search algorithm
- (2009) Esmat Rashedi et al. Natural Computing
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started