Abstract
Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis methods are compared. Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems. For example, the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data. Aiming at these problems, we propose some solutions and provide a comprehensive presentation for future research.
Highlights
The novel coronavirus pneumonia broke out in 2019 [1]
The novel coronavirus pneumonia is transmitted through interpersonal transmission [3], and the recent emergence of large numbers of infected people without initial symptoms of infection accelerates the spread of the disease [4], the surge in patients has put a lot of pressure on medical institutions [5]
Long-range connections were adopted in a sparing manner, and four convolution layers were leveraged as central hubs of long-connected much later layers in the network
Summary
The novel coronavirus pneumonia broke out in 2019 [1]. The pathogen is identified as a new enveloped ribonucleic acid-β (RNA-β) coronavirus, and it is similar to Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), named SARS-CoV-2 [2]. Most experiments encountered some problems, such as the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data We summarize these seven learning methods in this paper, and propose solutions to these problems encountered. Heidari et al [40] generated a pseudo-color image to improve the classification accuracy via two image preprocessing steps They collected 8,474 COVID-19 chest X-rays from several publicly available image databases. The bilateral low-pass filter was adopted to remove noise, and the Gaussian low-pass filter was used when calculating the weight They used the histogram equalization method to normalize the image. Pseudo-color image, two image preprocessing steps, binary image Gaussian low-pass filter bilateral low-pass filter histogram equalization method. Fuzzy technique, stacking technique Signal normalization, spatial normalization Contrast limited adaptive histogram equalization and normalization
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