ArticleNeural computing & applications2022
Efficient deep learning approach for augmented detection of Coronavirus disease.
Article in Neural computing & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.Journal of digital imaging · 2023Article
- Combating the COVID-19 infodemic using Prompt-Based curriculum learning.Expert systems with applications · 2023Article
- A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.Sensors (Basel, Switzerland) · 2023Article
- Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.Multimedia tools and applications · 2023Article
- COVID-19 and human development: An approach for classification of HDI with deep CNN.Biomedical signal processing and control · 2023Article
- ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection.Information sciences · 2023Article
- A Novel Optimized Perturbation-Based Machine Learning for Preserving Privacy in Medical Data.Wireless personal communications · 2023Article
- Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.Neural computing & applications · 2023Article
- DCU-Net: a dual-channel U-shaped network for image splicing forgery detection.Neural computing & applications · 2023Article
- Application of MobileNetV2 to waste classification.PloS one · 2023Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays.Expert systems with applications · 2022Article
- ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System.Arabian journal for science and engineering · 2022Article
- COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers.Sensors (Basel, Switzerland) · 2022Review
- TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.