ArticleComputer communications2021
Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio.
Article in Computer communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 69 citations in OpenAlex.
- Dental Caries diagnosis from bitewing images using convolutional neural networks.BMC oral health · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Analysis of Conocurvone, Ganoderic acid A and Oleuropein molecules against the main protease molecule of COVID-19 byEngineering analysis with boundary elements · 2023Article
- Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans.Bioengineering (Basel, Switzerland) · 2023Article
- MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.BMC medical informatics and decision making · 2023Article
- Socioeconomic impact due to COVID-19: An empirical assessment.Information processing & management · 2022Article
- Deep Neural Networks for Optimal Selection of Features Related to Flu.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Classifier Fusion for Detection of COVID-19 from CT Scans.Circuits, systems, and signal processing · 2022Article
- COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques.Computational and mathematical methods in medicine · 2022Article
- Improved COVID-19 detection with chest x-ray images using deep learning.Multimedia tools and applications · 2022Article
- A Secure and reliable RFID authentication protocol using digital schnorr cryptosystem for IoT-enabled healthcare in COVID-19 scenario.Sustainable cities and society · 2021Article
- Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.Healthcare (Basel, Switzerland) · 2021Review
- A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19.Diagnostics (Basel, Switzerland) · 2021Review
- DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.SN computer science · 2021Article
- Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods.Computational intelligence and neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The novel 2019 coronavirus disease (COVID-19) has infected over 141 million people worldwide since April 20, 2021. More than 200 countries around the world have been affected by the coronavirus pandemic. Screening for COVID-19, we use fast and inexpensive images from computed tomography (CT) scans. In this paper, ResNet-50, VGG-16, convolutional neural network (CNN), convolutional auto-encoder neural network (CAENN), and machine learning (ML) methods are proposed for classifying Chest CT Images of COVID-19. The dataset consists of 1252 CT scans that are positive and 1230 CT scans that are negative for COVID-19 virus. The proposed models have priority over the other models that there is no need of pre-trained networks and data augmentation for them. The classification accuracies of ResNet-50, VGG-16, CNN, and CAENN were obtained 92.24%, 94.07%, 93.84%, and 93.04% respectively. Among ML classifiers, the nearest neighbor (NN) had the highest performance with an accuracy of 94%.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.