A Complete Survey on Automatically Diagnosing COVID-19 In the field of Computer Vision and A Collection of Medical Images

Abstract

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.