ArticleInformation processing & management2022
Detection of COVID-19 using deep learning techniques and classification methods.
Article in Information processing & management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 57 citations in OpenAlex.
- Predicting pathological complete response to breast cancer neoadjuvant therapy using multi-combination machine learning models based on vision transformer features.Scientific reports · 2025Article
- A swarm intelligence-driven hybrid framework for brain tumor classification with enhanced deep features.Scientific reports · 2025Article
- A BiLSTM model enhanced with multi-objective arithmetic optimization for COVID-19 diagnosis from CT images.Scientific reports · 2025Article
- Predicting meningioma grades and pathologic marker expression via deep learning.European radiology · 2024Article
- Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- Artificial Intelligence in Stroke Imaging: A Comprehensive Review.The Eurasian journal of medicine · 2023Article
- Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal-Ethical Considerations.The Eurasian journal of medicine · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier.Healthcare (Basel, Switzerland) · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.Frontiers in public health · 2023Article
- Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey.New generation computing · 2023Article
- Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion.Computers in biology and medicine · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID-19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID-19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.
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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.