ArticleSN computer science2021
A Comprehensive Survey of COVID-19 Detection Using Medical Images.
Article in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation.Scientific reports · 2024Article
- Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media.Sensors (Basel, Switzerland) · 2023Review
- Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets.Bioengineering (Basel, Switzerland) · 2023Article
- Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Association between emphysema and other pulmonary computed tomography patterns in COVID-19 pneumonia.Journal of medical virology · 2023Article
- A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients.EJNMMI physics · 2022Article
- Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images.Pattern recognition · 2022Article
- Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods.Expert systems · 2022Article
- A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases.Biomedical signal processing and control · 2022Article
- DCML: Deep contrastive mutual learning for COVID-19 recognition.Biomedical signal processing and control · 2022Article
- COVID-19 image classification using deep learning: Advances, challenges and opportunities.Computers in biology and medicine · 2022Review
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN.Diagnostics (Basel, Switzerland) · 2022Article
- Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook.The Science of the total environment · 2022Review
- Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.Contrast media & molecular imaging · 2022Review
- Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study.Journal of healthcare engineering · 2022Review
- Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey.SN computer science · 2022Article
- COVID-19 classification of X-ray images using deep neural networks.European radiology · 2021Article
- An IoT-Based Deep Learning Framework for Early Assessment of Covid-19.IEEE internet of things journal · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.
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