As COVID-19 is the source of millions of deaths throughout the world, it turned obligatory to fight against the COVID-19 pandemic. Due to the need for expensive equipment, experienced radiologists, and the time-consuming in Reverse Transcription Polymerase Chain Reaction (RT-PCR) test, researchers find out the necessity to embrace X-ray images and Computed Tomography (CT) images based diagnosing. Wreak havoc of COVID-19 instigated me to review current emerging Artificial Intelligence(AI) based automatic diagnosing models through the statistical survey that will pave out the way of research. In this paper, I study different available research resources at the time span from April 2020 to July 2020. In order to help researchers in further research, I presented a statistical survey so that researchers can pick a preeminent diagnosing model. I took a look at 74 papers from April to July and specified preprocessing techniques, feature extraction, classification method, interpretability method, and experimental result. Moreover, I analyze training,testing and validation split ratio, as well as look into the dataset’s availability publicly. Some researchers are able to gain noticeable performance by adopting their own local model. On the contrary, some researchers adopt an existing pre-trained model and achieve the utmost result. Some models need to feed huge data and some models outperform despite having small data. In the following sections, all of the criteria will be illustrated briefly.
Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT-PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images. Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria. Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19. Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.
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