A survey of machine learning-based methods for COVID-19 medical image analysis
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Kashfia Sailunaz1, Tansel Özyer2, Jon Rokne1
1Department of Computer Science, University of Calgary, Calgary, AB, Canada.
Insights
This review surveys artificial intelligence and machine learning models for COVID-19 detection using medical images. It highlights recent advancements in image analysis for diagnosing and tracking the disease.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, has led to millions of deaths and infections globally.
- Accurate and rapid disease detection is crucial for pandemic control.
- Lung image analysis using AI and ML has emerged as a key area for COVID-19 detection.
Purpose of the Study:
- To provide a comprehensive review of recent AI/ML-based image analysis approaches for COVID-19 detection.
- To summarize research on medical image datasets, models (ML, DL, TL), and performance metrics.
- To discuss challenges and future research directions in AI for COVID-19 analysis.
Main Methods:
- Review of recent scientific literature on COVID-19 image analysis.
- Analysis of various machine learning, deep learning, and transfer learning models.
- Examination of chest X-ray, CT, and ultrasound image datasets.
Main Results:
- Numerous AI/ML models have been developed for COVID-19 detection, classification, segmentation, and severity assessment.
- Different image modalities and datasets show varying performance metrics for these models.
- The review consolidates information on existing research, datasets, and evaluation methods.
Conclusions:
- AI and ML offer powerful tools for analyzing medical images to combat COVID-19.
- Further research is needed to address challenges and enhance the efficiency of these models.
- This review serves as a valuable resource for understanding the current landscape and future potential of AI in COVID-19 diagnostics.
Abstract:
The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.
