A Comprehensive Survey of COVID-19 Detection Using Medical Images

Abstract

The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.

Introduction

Coronavirus Disease 2019 (COVID-19) is an infectious disease that started to proliferate from Wuhan China, in December 2019 [6] to make their results more accurate. Shi et al. [7] and Ilyas et al. [8] discussed some artificial intelligence-based models for diagnosis of COVID-19. In addition, Ulhaq et al. [9] reviewed some papers that worked on diagnosis, prevention, control, treatment, and clinical management of COVID-19. Besides, Ismael et al. [10] approached different types of Machine Learning and Deep Learning techniques COVID-19 detection working on X-ray images. Furthermore, a majority voting-based enseble classifier technique is employed by Chandra et al. [11]. However, as time goes by researchers are finding advanced and improved architectures for the diagnosis of COVID-19. In this paper, we have tried to review these new methods alongside with the basic structures of the earlier COVID-19 classification models. This survey will cover the research papers that are published or in pre-print format. Although it is not the most favorable approach due to the likelihood of below standard and research without peer-review, we intend to share all proposals and information in a single place while giving importance to the automatic diagnosis of COVID-19 in X-ray and CT images of lungs.

The fundamental aim of this paper is to systematically summarize the workflow of the existing researches, accumulate all the different sources of data sets of lung CT and X-ray images, sum up the frequently used methods to automatically diagnose COVID-19 using medical images so that a novice researcher can analyze previous works and find a better solution. We oriented our paper as follows:

• First, the Data set source and different types of images used in the papers are described in “COVID-19 Dataset and Resouce Description”.

• Second, the methodology where data preprocessing and augmentation techniques, feature extraction methods, classification, segmentation, and evaluation that researchers obtained are charactized in “Methodologies”.

• Finally, a discussion is made to aid the new researcher to find future works in detecting COVID-19.

COVID-19 Data Set and Resouce Description

The diagnosis of any disease is like the light at the end of the tunnel. In the case of the COVID-19 pandemic, the importance of earlier diagnosis and detecting the disease is beyond measure. The initial focus must be on the data by which we need to efficiently train a model. This data will help Machine Learning (ML) or Deep Learning (DL) algorithms to diagnose COVID-19 cases. Due to the disadvantages of RT-PCR, researchers adopted an alternative method which is the use of Artificial Intelligence on chest CT or X-ray images to diagnose COVID-19. Fundamentally, a chest CT image is an image taken using the computed tomography (CT) scan procedure, where X-ray images are captured from different angles and compiled to form a single image. A depiction of the CT images (COVID-19 infected and Normal) is illustrated in Fig. 2.

Fig. 2

Lung CT-scan images a COVID-19 affected, b normal

Although a CT scan consumes less time to demonstrate, it is fairly expensive. As a result, many researchers adopted X-ray images instead of CT images to develop a COVID-19 detection model. A chest X-ray is a procedure of using X-rays to generate images of the chest. In addition, it is relatively economical and convenient to maintain. X-ray images of different people with COVID-19, viral pneumonia, bacterial pneumonia, and a person without any disease (normal) are shown in Fig. 3. Furthermore, in this section, an overview of the data set sources used in the existing papers is characterized and data sets of both CT and X-ray images are illustrated and covered in this section.

Fig. 3

X-ray images a COVID-19, b viral pneumonia, c bacterial pneumonia, d normal from COVID19-XRay-data set

Data Set and Its Sources

Nowadays, the exchange of information between researchers and physicians creates difficulties due to the lockdown phase. Hence, massive COVID-19 data are out of reach or difficult to find for many researchers. As a deep learning architecture needs a considerable number of images to learn a model appropriately and efficiently, the existing COVID-19 automation researches are still in preliminary stages. However, some COVID-19 data sets are proposed and employed by the researchers which show exceptional results in detecting the COVID-19 affected lungs. To corroborate a beginner researcher, we have accumulated the abstract information of the data sets and their sources. A list of the data set sources from February 2020 to June 2020 is embellished in Table 1. In the following, we will cover both CT and X-ray images and their fundamental attributes.

Table 1 Summary of different data sources used in the papers

Some of the most popular data sets were collected from the following hospitals. Xu et al. [3] collected their data set from First Affiliated Hospital of Zhejiang University, the No. 6 People’s Hospital of Wenzhou, and the No. 1 People’s Hospital of Wenling. Song et al. [64] collected their data sets from three hospitals—Renmin Hospital of Wuhan University, and two affiliated hospitals (the Third Affiliated Hospital and Sun Yat-Sen Memorial Hospital) of the Sun Yat-sen University in Guangzhou. Chen et al. [73] built their data from the Renmin Hospital of Wuhan University (Wuhan, Hubei province, China). Shi et al. [

Fig. 5

Bar chart showing 18 publicly available X-ray data sets used from March, 2020 to June, 2020

Types and Properties of Images in the Data Set

Diseases such as Pneumonia, Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), Influenza and Tuberculosis affect the lungs such as COVID-19 which can lead to misclassification of X-ray and CT images. To avoid this problem, researchers have adapted their data set to have images of diseases affecting similar regions as COVID-19. Moreover, it is important to correctly distinguish COVID-19 patients from people who do not have COVID-19. For this purpose, the authors also used normal lung images collected from healthy people. These data sets are developed by combining COVID-19 images, other lung disease images such as Viral pneumonia [3,

Resizing is necessary, because the images are not always within the same estimate which postures an issue, whereas preparing the model. To generalize the data set all the images are resized into a fixed dimension such as 224 $\times$ 224 or 299 $\times$ 299.

Flip** or Rotating is done to increase the sample size of the data sets. Mainly horizontal and vertical flip** is used to do this as depicted in Fig. 9a.

Scaling or Crop** is the next most used augmentation technique is scaling or crop**. All the portions of the images are not necessary to use. Therefore, to reduce the redundancy researchers used the crop** method as illustrated in Fig. 9b.

Brightness or Intensity adjusting is mandatory to increase or reduce the brightness of the images. An example is shown in Fig. 9c.

As the COVID-19 data set is built with an insufficient number of COVID infected images, Generative Adversarial Networks (GAN) can be employed to generate COVID affected lung images which can be a path to avoid overfitting or data insufficiency. GAN is an unsupervised learning process structured on generative modeling embedded with deep learning architectures. It finds the patterns, similarities in the input data sets and generates new data which is similar to the input data set. GAN [93] increases the sample size in the data set but the quality of the samples is not guaranteed.

Fig. 9

Some examples of applying Pre-processing Techniques [a flip** by $180^{\circ }$, b crop**, and c adjusting brightness]

Table 4 Summary of the preprocessing and augmentation methods used by the papers

A representation of the papers—applying augmentation techniques on their model is characterized in Table 4 and the percentage usage of these augmentation techniques is depicted in Fig. 10. From there it can be seen that resize and flip** has the highest percentage of 27.9% and 27.0%, respectively. Scaling or Crop**, Contrast Adjusting, Brightness Adjusting, and GAN is 22.1%, 12.3%, 7.4%, and 3.3%, respectively. Besides these techniques, some authors used various traditional image preprocessing techniques such as Histogram Equalization [70], Adaptive Winner Filter [80], Affine Transformation [29, 40], Histogram Enhancement [40], Color Jittering [29].

Fig. 10

Pie chart illustrates the augmentation techniques used by different papers (Here the percentage of usage of six different augmentation techniques is shown)

Segmentation

It is necessary to train a model with the most significant features as unnecessary features or image region discredit the performance of the model. Therefore, extracting the Region of Interest (ROI) is the preeminent task before the training stage. For that purpose, segmentation comes into the hand as it can segregate the irrelevant and unnecessary regions of an image. In digital image processing and computer vision, image segmentation is defined as the technique of partitioning a digital image into different segments based on some pre-defined criteria, where a segment delineates as a set of pixels. Like other areas of medical image processing, segmentation boosts the effectiveness of COVID-19 detection by finding the ROI such as the lung region. Areas of the image that are redundant and not related to the significant feature area (out of the lung) could meddle the model performance. Using segmentation methods, only ROI areas are preserved which reduces this adverse effect of considering the out of the boundary features. Segmentation can be carried out manually by radiologists, but it takes a substantial amount of time. Several open-source automatic segmentation methods, such as region-based, edge-based, clustering, etc., are feasible to adopt. In the following, we will try to describe the prominent segmentation architecture and their properties.

The U-Net architecture is built with the help of Convolutional Neural Network (CNN) and it is modified such that it can achieve better segmentation in the domain of medical imaging [55]. The main advantage of U-Net is that the location information from the downsampling path and the contextual information in the upsampling path are combined to get general information—containing context and localization, which is the key to predicting a better segmentation map. U-Net-based strategies were utilized in [12,13,14, 17, 18, 38, 40, 61, 62, 66, 73, 74, 76, 77, 80, 81, 94, 95] for efficient and programmed lung segmentation extracting the lung region as the ROI.

For CT images, to keep contextual information between slices some researchers applied 3D versions of U-Net for lung segmentation named 3D U-Net ([3, 76]). Due to the low contrast at the infected areas in CT images and because of a large variety of both body shape, position over diverse patients, finding the infected areas from the chest CT scans was very challenging. Considering this issue, Narin et al. [27] developed a deep learning-based network named VB-Net. It is a modified 3D convolutional neural network based on V-Net [96]. In some other existing works, this segmentation method is adopted which alleviates the performance of the model [75, 83]. SegNet is also an efficient architecture for pixelwise denotation segmentation [97].

Segmentation methods, such as U-Net, Dense-Net, NABLA-N, SegNet, DeepLab, etc., were also used for the segmentation of lung images in different papers. The different segmentation methods used by different papers are illustrated in Table 5 and the number of papers in which a specific segmentation method is used is shown by a bar chart in Fig. 11.

Table 5 Summary of different segmentation methods used in COVID-19 detection

Fig. 11

Bar chart showing number of times different segmentation models used in different papers

Feature Extraction Methods

Feature extraction is an essential step for classification as the extracted features provide useful characteristics of the images. For image feature extraction, Deep Neural Networks have extraordinary capabilities to extract the important features from a large-scale data set. As a result, these are used extensively in computer vision algorithms and CNN which is also known as ConvNet. In the following, some of the feature extraction models are briefly described.

Convolutional Neural Network (CNN)

In visual imagery fields, CNN architectures are mostly employed and adopted methods [100]. A CNN architecture is built with various types of network layer—pooling layer, convolutional layer, flatten, etc. corroborating the development and performance of a model.

Convolution layer is the core building block of a CNN. The layer’s parameters are made up of a set of discoverable kernels or filters which have a little responsive field but enlarge through the full input volume. Non-linear layer is the layer, where the change of the output is not proportional to the change of the input. This layer uses activation functions to convey non-linearity to data by adding after each convolution layer. Used activation functions can be Rectified Linear Unit (ReLU) [101], Tanh, etc.

Pooling layer is another important part of CNN architecture, where it is used to downsize the matrix. Pooling can be done in several methods: Max Pooling, Min Pooling, Average Pooling, and Mean Pooling. Fully connected layer is the layer, where every Neuron of a layer is connected with every other neuron of another layer. Traditional Multilayer Perceptron neural networks (MLP) and this layer have common principles.

Existing Pre-trained CNN Models

Most of the COVID-19 diagnosis architectures used various pre-trained CNN models. A representation of the usage of this pre-trained model is shown in Table 6 (CT images) and Table 7 (X-ray images). To work with CT images, Residual Network (ResNet) [102], Densely Connected Convolutional Network (DenseNet) [103], Visual Geometry Group (VGG) [Interpretability

Fundamentally, a learning model consists of algorithms that try to learn patterns and relationships from the data source. To make the results obtained from machines interpretable, researchers use different techniques, such as Class Activation Map** (CAM), Gradient-weighted Class Activation Map** (Grad-CAM) based on a heatmap, Local Interpretable Model-agnostic Explanations (LIME) [110], and SHapley Additive exPlanations (SHAP) [111]. CAM is a method that creates heatmaps to show the important portions from the images, especially which regions are essential in terms of the Neural Network. CAM has various versions, such as Score CAM and Grad-CAM. The heatmap generated by CAM is a visualization that can be interpreted as where in the image the neural net is searching to make its decision. LIME tries to interpret models to guess the predictions of the predictive model in specific regions. LIME discovers the set of super pixels with the most coherent connection with the prediction label. It creates explanations by generating another data set of random disturbance by turning on and off a part of the super-pixels in the image. The aim of SHAP is to describe the forecast of a feature vector by calculating the contribution of distinct feature to the forecast. This is very important in image classification and object localization problems.

In our survey, there are few papers that utilized CAM [112] and few papers [

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1. Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

   Faisal Muhammad Shah, Sajib Kumar Saha Joy, Farzad Ahmed, Tonmoy Hossain, Mayeesha Humaira, Amit Saha Ami, Shimul Paul, Md Abidur Rahman Khan Jim & Sifat Ahmed

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Correspondence to Faisal Muhammad Shah.

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This article is part of the topical collection “Computer Aided Methods to Combat COVID-19 Pandemic” guest edited by David Clifton, Matthew Brown, Yuan-Ting Zhang and Tapabrata Chakraborty.

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Shah, F.M., Joy, S.K.S., Ahmed, F. et al. A Comprehensive Survey of COVID-19 Detection Using Medical Images. SN COMPUT. SCI. 2, 434 (2021). https://doi.org/10.1007/s42979-021-00823-1

• Received: 28 August 2020

• Accepted: 16 August 2021

• Published: 28 August 2021

• DOI: https://doi.org/10.1007/s42979-021-00823-1

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