ArticleExpert systems with applications2022
COVID-19 detection from CT scans using a two-stage framework.
Article in Expert systems with applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 73 citations in OpenAlex.
- Development and Validation of a Computed Tomography-Based Convolutional Neural Network Model for Assisting Donor Lung Acquisition in Lung Transplantation.Journal of imaging informatics in medicine · 2026Article
- A two-stage preprocessing and classification approach for accurate COVID-19 detection in X-ray images.Scientific reports · 2026Article
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Current Updates on Pathogenesis, Systemic Therapy, and Treatment of Invasive Fungal Infections.Current drug targets · 2025Review
- Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning.Interdisciplinary sciences, computational life sciences · 2023Article
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI).Journal of clinical medicine · 2023Article
- A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.Diagnostics (Basel, Switzerland) · 2023Article
- Squid Game Optimizer (SGO): a novel metaheuristic algorithm.Scientific reports · 2023Article
- EffViT-COVID: A dual-path network for COVID-19 percentage estimation.Expert systems with applications · 2023Article
- Deep learning models-based CT-scan image classification for automated screening of COVID-19.Biomedical signal processing and control · 2023Article
- UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.An international journal on information fusion · 2023Article
- A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans.PloS one · 2023Article
- Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms.Neural computing & applications · 2023Article
- CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19.Informatics in medicine unlocked · 2023Article
- Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme.PloS one · 2023Article
- Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms.Materials (Basel, Switzerland) · 2022Article
- A Secure Artificial Intelligence-Enabled Critical Sars Crisis Management Using Random Sigmoidal Artificial Neural Networks.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive
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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.