ArticleComputers in biology and medicine2021
An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Lightweight convolutional neural network for chest X-ray images classification.Scientific reports · 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 Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI.PloS one · 2024Article
- Feature-based deep neural network approach for predicting mortality risk in patients with COVID-19.Engineering applications of artificial intelligence · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images.Expert systems with applications · 2023Review
- Article
- Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images.Neural computing & applications · 2023Article
- Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review.Neural computing & applications · 2023Article
- Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images.Knowledge-based systems · 2022Article
- CCT: Lightweight compact convolutional transformer for lung disease CT image classification.Frontiers in physiology · 2022Article
- Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator.International journal of general medicine · 2022Article
- A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19.Frontiers in public health · 2022Article
- COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans.Computer methods and programs in biomedicine update · 2022Article
- A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.International journal of environmental research and public health · 2021Article
- A novel method for peanut variety identification and classification by Improved VGG16.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The new coronavirus disease known as COVID-19 is currently a pandemic that is spread out the whole world. Several methods have been presented to detect COVID-19 disease. Computer vision methods have been widely utilized to detect COVID-19 by using chest X-ray and computed tomography (CT) images. This work introduces a model for the automatic detection of COVID-19 using CT images. A novel handcrafted feature generation technique and a hybrid feature selector are used together to achieve better performance. The primary goal of the proposed framework is to achieve a higher classification accuracy than convolutional neural networks (CNN) using handcrafted features of the CT images. In the proposed framework, there are four fundamental phases, which are preprocessing, fused dynamic sized exemplars based pyramid feature generation, ReliefF, and iterative neighborhood component analysis based feature selection and deep neural network classifier. In the preprocessing phase, CT images are converted into 2D matrices and resized to 256 × 256 sized images. The proposed feature generation network uses dynamic-sized exemplars and pyramid structures together. Two basic feature generation functions are used to extract statistical and textural features. The selected most informative features are forwarded to artificial neural networks (ANN) and deep neural network (DNN) for classification. ANN and DNN models achieved 94.10% and 95.84% classification accuracies respectively. The proposed fused feature generator and iterative hybrid feature selector achieved the best success rate, according to the results obtained by using CT images.
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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.