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Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Sweta Bhattacharya1, Praveen Kumar Reddy Maddikunta1, Quoc-Viet Pham2
1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Insights
Deep learning effectively processes COVID-19 medical images, aiding in outbreak detection and healthcare system support. This review summarizes current research and discusses future directions for smart healthy cities.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Public Health
Background:
- The COVID-19 pandemic has significantly impacted global health systems.
- Deep learning (DL) has demonstrated utility in various medical imaging applications.
- Medical imaging (X-ray, CT, MRI) is crucial for diagnosing COVID-19.
Purpose of the Study:
- To summarize state-of-the-art deep learning applications in COVID-19 medical image processing.
- To provide an overview of deep learning in healthcare over the last decade.
- To present case studies and discuss challenges in DL for COVID-19 medical image analysis.
Main Methods:
- Systematic review of recent research on deep learning for COVID-19 medical images.
- Literature review of deep learning applications in healthcare.
- Analysis of case studies from China, Korea, and Canada.
Main Results:
- Numerous deep learning research works have emerged for COVID-19 medical image processing since early 2020.
- Deep learning techniques are being applied to analyze X-rays, CT scans, and MRIs for COVID-19 detection.
- Case studies illustrate successful implementation of DL in different healthcare settings.
Conclusions:
- Deep learning shows significant promise in combating the COVID-19 outbreak through medical image analysis.
- Further research is needed to address challenges in DL implementation for widespread adoption.
- Advancements in DL for medical imaging can contribute to smart healthy cities and crisis management.
Abstract:
Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.
