Purpose: 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 con- ventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images like X-Ray and Computed Tomography (CT) images to detect this disease with the help of Arti cial Intelligence (AI) based systems, to assist in automating the scanning procedure. Methods: 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 col- lected information about available research resources and inspected a total of 80 papers till June 20, 2020. Results: We explored and analyzed datasets, preprocessing techniques, seg- mentation methods, feature extraction, classi cation, and experimental results which can be helpful for nding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also re ected that there is a scarcity of annotated medical images/datasets of COVID-19 a ected people, which requires enhancing, segmentation in prepro- cessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. Conclusion: This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classi cation. Keywords COVID-19 Deep Learning Medical Image Survey AI Faisal Muhammad Shah (Corresponding Author) Associate Professor Department of Computer Science and Engineering Ahsanullah University of Science and Technology E-mail: faisal.cse@aust.edu 2 Faisal Muhammad Shah et al. 1 Introduction Coronavirus Disease 2019 (COVID-19) is an infectious disease that started to proliferate from Wuhan China, in December 2019 [1]. Within a short pe- riod of time, this disease is ravaged every corner of the world and the World Health Organization declared this disease as a pandemic on 11 March 2020 [2]. This disease is caused by the strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) . In July 2020, cases reached almost 12 million worldwide, and death due to this disease kept rising day by day, and the death toll is 562,039 [3]. From the Worldometers data, the total deaths and total cures (based on month) is illustrated in Fig. 1 [3]. Observing the statistics and properties of COVID-19 it can be asserted that this life-threatening virus can unfurl from individual to individual via cough, sneezing, or even close contact. As a result, it has become important to detect the a ected people earlier and isolate them to stop further spreading of this virus. RT-PCR is a procedure of assembling samples from a region of a person's body where the coronavirus is most likely to congregate, such as a person's nose or throat. Then this sample is treated with chemicals to track down the existence of the coronavirus. Though RT-PCR can distinctly identify coron- avirus disease, it has a high false-negative rate where the model predicts the result as negative but actually, it is positive (false-negative). Furthermore, in many regions of the world RT-PCR's availability is limited. Hence, medical images like Computer Tomography (CT) and X-Ray images can be the next best alternative to detect this virus as most of the medical/hospital commonly have this apparatus to generate images. Also, CT or X-Ray images are read- ily available where there is no RT-PCR. Moreover, RT-PCR is expensive and consumes a considerable amount of time for the identi cation. Additionally, proper training is required for the health workers to collect samples for PCR whereas, it is relatively easy to handle and produce CT and X-Ray images. To work on these medical images, deep learning methods are the most conventional and might be the only direction. Deep Learning is an emerging eld that could play a signi cant role in the detection of COVID-19 in the future. Till now researchers have used machine learning/deep learning mod- els to detect COVID-19 using medical images such as X-Ray or CT images and obtained promising results. Many researchers also used transfer learning, attention mechanism [4], and Gradient-weighted Class Activation Mapping (Grad-CAM) [5] to make their results more accurate. Shi F et al. [6] and Ilyas M et al. [7] discussed some arti cial intelligence-based models for diagnosis of COVID-19. Also, Ulhaq A et al. [8] reviewed some papers that worked on diagnosis, prevention, control, treatment, and clinical management of COVID- 19. However, as time goes by researchers are nding 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 classi cation models. This survey will cover the research papers that are published or in pre-print format. Although it is not the most favor- able approach due to the likelihood of below standard and research without A Comprehensive Survey of COVID-19 Detection using Medical Images 3 Fig. 1: Total Case, Total Death, and Total Cured (By month) from Worldome- ter 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 work ow of the existing researches, accumulate all the di erent sources of datasets 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 nd a better solution. We oriented our paper as follows: {Firstly, the Dataset source and di erent types of images used in the papers are described in section 2. {Secondly, the methodology where data preprocessing and augmentation techniques, feature extraction methods, classi cation, segmentation, and evaluation that researchers obtained are charactized in section 3. {Lastly, a discussion is made to aid the new researcher to nd future works in detecting COVID-19. 2 COVID-19 Dataset 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 eciently train a model. This data will help Machine Learning (ML) 4 Faisal Muhammad Shah et al. 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 Arti cial 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 di erent angles and compiled to form a single image. A depiction of the CT images (COVID-19 infected and Normal) is illustrated in Fig. 2. (a) (b) Fig. 2: CT images (a) COVID-19 (b) Normal. Although a CT scan consumes less time to demonstrate, it is fairly ex- pensive. 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 proce- dure of using X-Rays to generate images of the chest. Also, it is relatively economical and convenient to maintain. X-Ray images of di erent people with COVID-19, viral pneumonia, bacterial pneumonia, and a person without any disease (normal) are shown in Fig. 3. Further in this section, an overview of the dataset sources used in the existing papers is characterized and datasets of both CT and X-Ray images are illustrated and covered in this section. 2.1 Dataset and its Sources Nowadays, the exchange of information between researchers and physicians creates diculties due to the lockdown phase. Hence, massive COVID-19 data are out of reach or dicult to nd for many researchers. As a deep learning architecture needs a considerable number of images to learn a model appro- priately and eciently, the existing COVID-19 automation researches are still in preliminary stages. However, some COVID-19 datasets are proposed and employed by the researchers which show exceptional results in detecting the A Comprehensive Survey of COVID-19 Detection using Medical Images 5 (a) (b) (c) (d) Fig. 3: X-Ray images (a) COVID-19 (b) Viral Pneumonia (c) Bacterial Pneu- monia (d) Normal from COVID19-XRay-Dataset COVID-19 a ected lungs. To corroborate a beginner researcher, we have ac- cumulated the abstract information of the datasets and their sources. A list of the dataset sources from February 2020 to June 2020 is embellished in Ta- ble 1. In the following, we will cover both CT and X-Ray images and their fundamental attributes. Table 1: Summary of di erent data sources used by papers TYPE DATASET NAME DATASET SOURCE PAPERS CT The Lung Image Database Consortium (LIDC) https://doi.org/10.1118/1.3528204 [9], [10] CT China University Hospitals of Geneva (HUG) www.ChainZ.cn, El-Camino Hospital (CA), Zhejiang Province [11] CT Societa Italiana di Radiologia Medica e Interventistica https://www.sirm.org/ [12] CT Lung Segmentation and Candidate Points Generation https://www.kaggle.com/arturscussel/lung-segmentation-and-candidate-points-generation [13] CT COVID-19 CT segmentation dataset http://medicalsegmentation.com/COVID19/ [14], [15], [16], [17] CT COVID-CT https://github.com/UCSD-AI4H/COVID-CT [18] X-Ray and CT COVID-19 X rays https://www.kaggle.com/andrewmvd/convid19-X-rays [19], [20], [21], [22] X-Ray and CT BIMCV COVID-19+ https://bimcv.cipf.es/bimcv-projects/bimcv-COVID19/ [23] X-Ray COVID-chestxray-dataset https://github.com/ieee8023/COVID-chestxray-dataset[8], [24], [25], [26], [19], [27], [28], [29], [13], [30], [31], [32], [20], [ ?], [33], [34], [35], [36], [37], [38], [21], [39], [40], [22], [41], [42], [43], [44] X-Ray Chest X-Ray Images (Pneumonia) https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia [24], [25], [28], [29], [31], [ ?], [45], [43] X-Ray COVID-19 Radiography Database https://www.kaggle.com/tawsifurrahman/COVID19-radiography-database [46], [47], [48], [41], [49] X-Ray British Society of Thoracic (BSTI) https://www.bsti.org.uk/training-and-education [28] X-Ray Radiopedia https://radiopaedia.org/articles/normal-chest-imaging-examples?lang=gb [28], [30], [33], [50], [42] X-Ray COVID-19 Chest X-ray Dataset Initiative https://github.com/agchung/Figure1-COVID-chestxray-dataset [47], [48] X-Ray ActualMed COVID-19 Chest X-ray Dataset Initiative https://github.com/agchung/Actualmed-COVID-chestxray-dataset [47], [48] X-Ray COVID-19 Image Data Collection https://arxiv.org/abs/2003.11597 [51], [48] X-Ray COVID-19 database https://www.sirm.org/category/senza-categoria/COVID-19/ [51], [52], [30], [53], [33], [49] X-Ray Optical Coherence Tomography (OCT) https://data.mendeley.com/datasets/rscbjbr9sj/2. [54], [22], [44] X-Ray COVID-19 X-ray dataset (COVID-CAPS) https://github.com/ShahinSHH/COVID-CAPS [55] X-Ray Kaggle RSNA Pneumonia Detection Dataset https://www.kaggle.com/c/rsna-pneumonia-detection-challenge [47], [32], [56], [35], [36], [48], [39], [42] X-Ray NIH Chest X-ray Dataset https://www.kaggle.com/nih-chest-xrays/data [ ?], [57] X-Ray Pneumonia Classi cation https://drive.google.com/open?id=1J9I-pPtPfLRGHJ42or4pKO2QASHzLkkj [50] X-Ray COVID-19 https://github.com/muhammedtalo/COVID-19 [57] X-Ray COVIDGR-1.0 https://github.com/ari-dasci/COVIDgr [58] 2.2 CT Image Sources As CT images are said to be detailed than X-Ray images, the diagnosis of COVID-19 and developing a model becomes more convenient by employing the CT-scan images. For CT images-based works, four papers used the COVID-19 CT segmentation dataset to develop a classi cation architecture. This dataset contains hundred axial CT images from forty patients ([14], [15], [16], [17]). Chen X et al. [14] and Qiu Y et al. [16] achieved 89% and 83.62% accuracy respectively using this dataset. Furthermore, two authors adopted the Lung Image Database Consortium (LIDC) dataset and accomplished an accuracy above 90% ([9], [10]). Besides these, some authors used Societa Italiana di 6 Faisal Muhammad Shah et al. Radiologia Medica e Interventistica to generate datasets [12], Lung Segmenta- tion, and Candidate Points Generation [13], COVID-CT [18] and HUG dataset [11] for their purpose. A representation of these dataset sources is character- ized in Table 1 and depicted in Fig. 4 (based on months). From the table, we can infer that the COVID-19 CT segmentation dataset [14], [15], [16], [17] was used mostly in April 2020 and Lung Image Database Consortium (LIDC) dataset was used in March 2020 and June 2020. Some researchers also used other lung disease images apart from these mostly used datasets. Nevertheless, they collected CT images from di erent hospitals to build these datasets. Fig. 4: A bar chart showing seven publicly available CT datasets used from March, 2020 to June, 2020. 2.3 X-Ray Image Sources X-Ray image dataset is more available than the CT images as the cost of capturing an X-ray image is considerably more economical than a CT im- age. Studying the existing literature, most of the authors used the COVID- chest X-ray dataset ([59], [29], [39], [44]). Moreover, Kaggle RSNA Pneumo- nia Detection Dataset ([47], [32], [42]), COVID-19 database ([51], [30], [49]), Chest X-Ray Images (Pneumonia) ([24], [56], [43]) is adopted to evaluate their model. These are the most common dataset for Chest X-Ray based COVID- 19 research (Table 1). However, these datasets contain a limited number of COVID-19 infected lung images which is not ecient to train a deep learn- ing model as the model can over t the data. For this purpose, most of the researchers utilized di erent preprocessing techniques to increase the dataset A Comprehensive Survey of COVID-19 Detection using Medical Images 7 size, one of them is data augmentation. Furthermore, the existing works are trained on a hybrid dataset combining the COVID-19 dataset and normal lung images from another repository. For X-Ray based works, Al-antari MA et al. [41] used COVID-19 Radiography Database for alternative lung diseases. An illustration of the eighteen X-Ray dataset usage is depicted in Fig. 5. From there it can be noticed that the COVID-chest x-ray dataset was used by most of the authors followed by Kaggle's Chest X-Ray images (Pneumonia) which was used mostly in March 2020, April 2020 and June 2020. Some papers also used both CT and X-ray images from the COVID-19 X rays and BIMCV COVID-19+ datasets. From both Fig. 4 and Fig. 5 it can be observed that BIMCV COVID-19+ emerged in June 2020 in terms of developing a COVID- 19 classi cation model. Fig. 5: A bar chart showing eighteen publicly available X-Ray datasets used from March, 2020 to June, 2020 2.4 Types and Properties of Images in the Dataset Diseases such as Pneumonia, Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), In uenza and Tuberculosis af- fect the lungs like COVID-19 which can lead to misclassi cation of X-Ray and CT images. To avoid this problem, researchers have adapted their dataset to have images of diseases a ecting 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 im- ages collected from healthy people. These datasets are developed by combining 8 Faisal Muhammad Shah et al. Table 2: Summary of di erent type of lung disease and normal (healthy pa- tients) CT images used by papers Paper Normal Pneumonia COVID-19 Other Lung Diseases [3] 175 224 219 N/A [60] 86 100 88 N/A [69] N/A 14469 20886 N/A [70] 1325 1735 1296 N/A [71] N/A 1027 1658 N/A [72] 91 NS 877 450 [9] 100 N/A 106 N/A [64] N/A 40 1266 N/A [73] N/A N/A N/A N/A [61] 28873 30345 14944 15466 [28] 153 N/A 203 N/A [11] 1126 N/A 938 N/A [12] 6000 N/A 6000 N/A [13] 247 N/A 178 N/A [14] NS NS NS N/A [74] N/A N/A 150 N/A [75] 75541 N/A 64771 N/A [76] 397 N/A 349 N/A [77] 195 N/A 275 N/A [18] 339 N/A 391 N/A [15] N/A N/A 373 N/A [78] 495 N/A 449 N/A [16] N/A N/A 100 N/A [79] NS 740 325 N/A [53] 344 N/A 439 N/A [80] N/A 1027 1495 N/A [17] N/A N/A N/A N/A [81] 397 N/A 349 83 [10] 3308 2296 2228 N/A [82] 463 N/A 349 N/A Total 120438 50268 121700 15999 COVID-19 images, other lung disease images like Viral pneumonia ([3], [25], [19], [46], [20]), Bacterial pneumonia ([60], [19], [61], [62], [20], [63]), fungal pneumonia [64], SARS ([65], [66], [67], [57]), MERS [66], In uenza [10], Tu- berculosis ([61], [67], [68]) and images of healthy people. The distribution of di erent types of lung disease or normal images and the number of CT images used by papers are illustrated in Table 2. There `Not speci ed (NS)' indicates that papers used that type of image but did not state the number explicitly and `N/A' indicates those types of images were not used. Furthermore, the number of di erent types of CT images used in the papers is presented in Fig. 6. From there it can be seen that the number of COVID- 19 CT images used for classi cation is 121,700. A total number of normal CT Images, Pneumonia CT Images, and Other lung disease CT images are 120,438, 50,268, and 15,999 respectively. Additionally, the distribution of di erent types of X-Ray images is depicted in Table 3 where the total number of di erent images used in fty research A Comprehensive Survey of COVID-19 Detection using Medical Images 9 Fig. 6: The total number of CT images of di erent diseases and normal CT images used from February 2020 to June 2020. works from February to June is represented. In Table 2, the distribution of di erent types of images was shown for thirty papers which were also from February 2020 to June 2020. Moreover, `Not speci ed (NS)' and `N/A' are used in Table 3 with the same purpose as it did in Table 2. A depiction of the total number of COVID-19, normal, Pneumonia, and other lung disease X-Ray images used by papers is shown in Fig. 7. From the gure, it can be seen that fty papers used 21,062 COVID-19 images, 168,223 normal images, 127,456 Pneumonia images, and 114,094 other lung disease images were used in total. It can be said that more CT images of COVID-19 were used than COVID-19 X-Ray images by comparing Fig. 6 and Fig. 7. 3 Methodologies After data collection, several preeminent steps must be followed to diagnose COVID-19, hence this section depicts di erent techniques employed by di er- ent papers. Firstly, preprocessing techniques along with their characteristics and properties is described. Secondly, feature extraction methods are thor- oughly discussed. After that, segmentation methods and classi cation tech- niques are reviewed. Lastly, the results obtained in the existing studied papers are brie y described. The work ow of diagnosing COVID-19 from X-Ray im- ages demonstrated in Fig. 8. 10 Faisal Muhammad Shah et al. Table 3: Summary of di erent type of lung disease and normal (healthy pa- tients) X-Ray images used by papers Paper Normal Pneumonia COVID-19 Other Lung Diseases [59] 25 N/A 25 N/A [24] 50 N/A 50 N/A [25] 504 700 224 N/A [26] N/A 1431 100 N/A [19] 1583 4290 68 N/A [46] 1579 1485 423 N/A [27] 15 N/A 25 N/A [28] 85 N/A 85 N/A [47] N/A 5551 358 N/A [51] 3450 N/A 455 N/A [83] 8066 5538 259 N/A [29] 207 N/A 207 N/A [30] N/A 320 135 N/A [84] 179 179 179 N/A [31] 310 654 284 N/A [62] 7595 8792 313 N/A [55] N/A N/A 120 N/A [32] 8851 6054 180 N/A [85] 2400 2600 536 N/A [20] 127 127 127 N/A [56] 137 N/A 137 N/A [33] 350 322 225 50 [34] 8066 5521 183 N/A [35] 44993 14777 167 N/A [36] 9039 2306 318 N/A [86] 8851 6045 215 N/A [37] 1591 4706 105 N/A [38] 191 131 180 N/A [50] 1000 54 90 N/A [87] N/A N/A 462 N/A [48] 8066 5551 358 N/A [65] 80 N/A 105 11 [21] 1000 1000 239 N/A [66] N/A N/A 423 287 [88] 1579 2760 462 N/A [39] 15000 15000 99 N/A [40] 8851 6045 386 N/A [67] 1126 Not speci ed 1050 112566 [68] 388 1000 500 303 [89] 2880 5179 415 N/A [22] 1000 2000 309 N/A [45] 25 N/A 25 N/A [41] N/A 120 326 866 [57] 3520 500 250 11 [63] N/A 1583 305 N/A [49] 13410 13450 8640 N/A [42] 554 554 154 N/A [43] 505 512 236 N/A [58] 377 N/A 377 N/A [44] 668 619 132 N/A Total 168223 127456 21062 114094 A Comprehensive Survey of COVID-19 Detection using Medical Images 11 Fig. 7: The total number of X-Ray images for di erent disease and normal patients used from February, 2020 to June, 2020 Fig. 8: The overall methodology of diagnosing COVID-19 from X-Ray images 3.1 Preprocessing Techniques There is a high chance that a COVID-19 dataset is built with some obscure, duplicate, blur, etc. images that degrade the performance of a model. Hence, it is necessary and mandatory to perform preprocessing techniques on redundant images. Various types of preprocessing techniques can be carried out based on the diculties of the dataset. One of the major problems of deep learning is over tting. To minimize the e ect of over tting data augmentation is used in the pre-processing stage. Resizing, scaling, cropping, ipping, rotating are the most employed data augmentation techniques. Some of these data augmenta- tion techniques are discussed below: {Resizing is necessary because the images are not always within the same estimate which postures an issue whereas preparing the model. To gener- 12 Faisal Muhammad Shah et al. alize the dataset all the images are resized into a xed dimension like 224 224 or 299 299. {Flipping or Rotating is done to increase the sample size of the datasets. Mainly horizontal and vertical ipping is used to do this as depicted in Fig. 9a. {Scaling or Cropping is the next most used augmentation technique is scal- ing or cropping. All the portions of the images are not necessary to use. So, to reduce the redundancy researchers used the cropping 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 dataset is built with an insucient number of COVID infected images, Generative Adversarial Networks (GAN) can be employed to generate COVID a ected lung images which can be a path to avoid over tting or data insuciency. GAN is an unsupervised learning process structured on generative modeling embedded with deep learning architectures. It nds the patterns, similarities in the input datasets and generates new data which is similar to the input dataset. GAN [90] increases the sample size in the dataset but the quality of the samples is not guaranteed. (a) (b) (c) Fig. 9: Some examples of applying Pre-processing Techniques [(a) ipping by 180 degree, (b) Cropping, and (c) adjusting brightness] A representation of the papers | applying augmentation techniques on their model is characterized in Table 4 and the percentage usage of these aug- mentation techniques is depicted in Fig. 10. From there it can be seen that resize and ipping has the highest percentage of 27.9% and 27.0% respectively. Scaling or Cropping, Contrast Adjusting, Brightness Adjusting, and GAN is A Comprehensive Survey of COVID-19 Detection using Medical Images 13 Table 4: Summary of the preprocessing and augmentation methods used by the papers Augmentation Techniques Paper Count Resize[70], [59], [24], [25], [64] ,[26], [19], [46], [27], [28], [47], [51], [11], [12], [29], [14], [74], [31], [62], [18], [32], [15], [56], [38], [39], [40], [68], [89], [22], [45], [57], [63], [49], [43]34 Flipping or Rotating[3], [72], [72], [26], [19], [46], [47], [51], [12], [83], [29], [14], [30], [74], [75], [55], [32], [85], [16], [34], [86], [87], [48], [65], [81], [82], [39], [67], [45], [41], [57], [49], [44]33 Scaling or Cropping[3], [71], [72], [9], [25], [64], [26], [19], [28], [12], [29], [14], [74], [75], [62], [55], [18], [32], [16], [34], [86], [48], [82], [39], [89], [41], [43]27 Contrast adjusting[73], [19], [27], [51], [29], [84], [74], [77], [79], [87], [65], [66], [82], [68], [49]15 Brightness or Intensity adjusting [73], [47], [12], [29], [84], [34], [86], [48], [82] 9 GAN [54], [18], [17], [58] 4 22.1%, 12.3%, 7.4%, and 3.3% respectively. Besides these techniques, some authors used various traditional image preprocessing techniques such as His- togram Equalization [66], Adaptive Winner Filter [77], Ane Transformation [26], [37], Histogram Enhancement [37], Color Jittering [26]. Fig. 10: Pie chart illustrates the augmentation techniques used by di erent papers (Here the percentage of usage of six di erent augmentation techniques is shown) 14 Faisal Muhammad Shah et al. 3.2 Segmentation It is necessary to train a model with the most signi cant features as unnec- essary features or image region discredit the performance of the model. So, 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 de ned as the tech- nique of partitioning a digital image into di erent segments based on some pre-de ned criteria where a segment delineates as a set of pixels. Like other areas of medical image processing, segmentation boosts the e ectiveness of COVID-19 detection by nding the region of interest (ROI) like the lung re- gion. Areas of the image that are redundant and not related to the signi cant feature area (out of the lung) could meddle the model performance. By using segmentation methods, only ROI areas are preserved which reduces this ad- verse e ect 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 prop- erties. The U-Net architecture is built with the help of CNN (Convolutional Neu- ral Network) and it is modi ed such that it can achieve better segmentation in the domain of medical imaging [52]. The main advantage of U-Net is that the location information from the downsampling path and the contextual in- formation 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 ([69], [70], [72], [9], [73], [11], [14], [74], [77], [62], [15], [78], [10], [35], [37], [58]) for ecient and programmed lung segmentation extracting the lung region as the region of interest (ROI). For CT images, to keep contextual information between slices some re- searchers applied 3D versions of U-Net for lung segmentation named 3D U- Net ([3] [72]). 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, nding the infected areas from the chest CT scans was very challenging. Con- sidering this issue, Fei Shan et al. [24] developed a deep learning-based network named VB-Net. It is a modi ed 3D convolutional neural network based on V- Net [91]. In some other existing works, this segmentation method is adopted which alleviates the performance of the model ([71] [80]). Additional segmentation methods such as OpenCV, Dense-Net, NABLAN, DeepLab were also used for the segmentation of lung images in di erent papers. The di erent segmentation methods used by di erent papers are illustrated in Table 5 and the number of papers in which a speci c segmentation method is used is shown by a bar chart in Fig. 11. A Comprehensive Survey of COVID-19 Detection using Medical Images 15 Table 5: Summary of di erent segmentation methods used in COVID-19 de- tection Segmentation Methods Papers Count U-Net[69], [70], [72], [9], [73], [11], [14], [74], [77], [62], [15], [78], [10], [35], [37], [58]16 3D U-Net [3], [72] 2 VB-Net [71], [80] 2 OpenCV [60] 1 DeepLab [14] 1 NABLAN [13] 1 Dense-Net [64], [38] 2 MiniSeg [16] 1 Fig. 11: A bar chart showing number of times di erent segmentation models used in di erent papers 3.3 Feature Extraction Methods Feature extraction is an essential step for classi cation as the extracted fea- tures provide useful characteristics of the images. For image feature extraction, Deep Neural Networks (DNN) have extraordinary capabilities to extract the important features from a large-scale dataset. As a result, these are used ex- tensively in computer vision algorithms and Convolutional Neural Network (CNN) which is also known as ConvNet. In the following, some of the feature extraction models are brie y described. 3.3.1 Convolutional Neural Network (CNN) In visual imagery elds, CNN architectures are mostly employed and adopted methods [92]. A CNN architecture is built with various types of network layer 16 Faisal Muhammad Shah et al. | pooling layer, convolutional layer, atten, etc. corroborating the develop- ment and performance of a model. Convolution layer is the core building block of a CNN. The layer's param- eters are made up of a set of discoverable kernels or lters which have a little responsive eld 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 Recti ed Linear Unit (ReLU) [93], 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. 3.3.2 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 im- ages, Residual Network (ResNet), Densely Connected Convolutional Network (DenseNet), Visual Geometry Group (VGG), SqueezeNet architecture are the most adopted pre-trained architectures by researchers (Table 6) and ResNet, DenseNet, VGG, Inception, InceptionResNet models are employed for X-Ray images (Table 7). Some of the most used existing pre-trained CNN models are described in the following. Residual Network (ResNet) is a CNN architecture, designed to enable hun- dreds or thousands of convolutional layers. While previous CNN architectures had a drop o in the e ectiveness of additional layers, ResNet [94] can e- ciently add a large number of layers leads to strong performance as an outcome of the model. ResNet is convenient and ecient for data-driven approaches. It has di erent variants such as | ResNet18, ResNet169, ResNet50, ResNet152, etc. focusing on distinct perspectives. Moreover, studying the works we can infer that, ResNet is the most used architecture for both CT and X-Ray based COVID-19 research. Fourteen papers that have used ResNet in their proposed models for CT image-based works are shown in Table 6 and twenty-seven papers are used for X-Ray based works are represented in Table 7. Densely Connected Convolutional Networks (DenseNet) is one of the cur- rent neural networks for visual object recognition [95]. It is quite similar to the architecture ResNet but has some fundamental di erences. This model ensures maximum information ow between the layers in the network that helps to extract the optimal features. By matching feature map size all over the network, the authors connected all the layers directly to all of their sub- sequent layers | a Densely Connected Neural Network, or simply known as A Comprehensive Survey of COVID-19 Detection using Medical Images 17 DenseNet. DenseNet made strides in the data stream between layers by propos- ing these distinctive network designs. Unlike many other networks like ResNet, DenseNets do not sum the output feature maps of the layer with the incoming feature maps but concatenate them. This architecture has di erent types of variants (DenseNet101, DenseNet169, DenseNet201, etc.) and it has an input shape of 224 x 224. In Table 6 (CT image), DenseNet architecture is used by four papers, and from Table 7 (X-Ray image), it is used by seventeen papers. Visual Geometric Group (VGG) is another important CNN architecture for the purpose of feature extraction. VGG Network consists of 16 or 19 convolu- tional layers and is very convenient to demonstrate because of its very uniform architecture [96]. In our survey, we studied four papers who work with VGG for COVID-19 detection purposes to get the features from CT images that were illustrated in Table 6, and fteen papers from X-Ray based works were shown in Table 7. Inception is a transfer learning-based method consists of two segments: feature extraction from images with the help of CNN and classi cation with softmax and fully connected layers [97] [98]. Various versions of Inception architectures are used in the medical imaging eld. Among these, InceptionV1, InceptionV2, InceptionV3, and InceptionV4 are the prominent ones with an input image shape of 299 x 299. Twelve papers used an Inception based model for X-Ray based classi cation of COVID-19 given in Table 6 and only one paper [79] for CT images used this model to classify COVID-19 disease. InceptionResnet is the similar architecture as InceptionV4 [99]. Inception- ResNetv1 is a half breed Initiation adaptation that encompasses a similar computational fetched to Inceptionv3. InceptionResNetV2 is a convolutional neural arrangement that is prepared on more than a million pictures from the ImageNet [100] database. The arrangement is 164 layers profound and can classify images into 1000 distinct categories. Eight papers are given in Table 7 utilized this strategy for X-Ray pictures including extraction. Table 6: Summary of image feature extraction methods used by di erent pa- pers for CT images CNN Paper Count ResNet [3],[60],[70],[72],[9],[61],[11],[12],[75],[76],[17],[81],[10],[82] 14 DenseNet [60],[64],[76],[82] 4 VGG [60],[12],[75],[76] 4 SqueezeNet [53],[81] 2 AlexNet [28] 1 CrNet [76] 1 EcientNet [[76] 1 FCN-8s [72] 1 GoogLeNet [12] 1 Inception [79] 1 The number of di erent CNN models used for CT Images is shown in Fig. 12 (based on month). For feature extraction from CT images, researchers used 18 Faisal Muhammad Shah et al. various types of CNN models from which ResNet is the most used architec- ture within these ve months. In February 2020 three types of CNN models are used, two papers used ResNet, one paper used DenseNet, and another paper used VGG. During March 2020 four papers used ResNet, and AlexNet and FCN-8s used once. Whereas in April 2020 ResNet was used four times, three papers used VGG and SqueezeNet, CrNet, EcientNet, GoogLeNet, and Inception are used once. Moreover, in May 2020 ResNet is used twice and SqueezeNet is used once, and nally, in June 2020 DenseNet and ResNet are used by one and two papers respectively. Fig. 12: A bar chart for describing used CNN for CT images (By month) Di erent CNN models and the number of times of its usage per month is shown for X-Ray images in Fig. 13. where ResNet is one of the most used mod- els for feature extraction. In our survey, during March 2020, ResNet is used six times, DenseNet two times, VGG three times, Inception four times, Inception- ResNet three times, Xception two times, and AlexNet, SqueezeNet one times each. In April 2020 ResNet is used ten times, DenseNet eight times, VGG six times, Inception three times, InceptionResNet three times and Xception ve times. AlexNet, GoogLeNet, and ShueNet are both used twice. SqueezeNet, Inception, and InceptionResNet are used three times each. NASnet and Ef- cientNet are used once each time. During May 2020 ResNet is used ve times, DenseNet and Inception are used two times each, VGG, SqueezeNet, and GoogLeNet three of them are used only once. Finally, in June 2020 ResNet is used seven times, DenseNet and VGG both are used ve times each. Incep- tion, InceptionResNet, Xception, AlexNet, GoogLeNet, and NASnet are used three, two, one, three, one, and two times respectively. A Comprehensive Survey of COVID-19 Detection using Medical Images 19 Table 7: Summary of image feature extraction methods used by di erent pa- pers for X-Ray images CNN Papers Count ResNet[59],[24],[26],[19],[46],[47],[54],[83],[30],[32],[85],[20],[56],[34], [36],[86],[37],[38],[65],[21],[66],[88],[39],[40],[67],[68],[43],[58]27 DenseNet[59],[46],[83],[29],[84],[62],[20],[56],[35],[86],[37],[66],[88], [39],[68],[22],[43]17 VGG[59],[25],[46],[83],[30],[62],[20],[56],[34],[21],[67],[68],[89], [45],[57]15 Inception [59],[24],[25],[46],[62],[20],[56],[38],[66],[88],[49],[43] 12 InceptionResNet [59],[24],[25],[62],[20],[56],[39],[43] 8 Xception [59],[25],[31],[62],[32],[20],[56],[44] 8 AlexNet [28],[54],[20],[67],[68],[42] 6 SqueezeNet [46],[54],[84],[85],[66] 5 GoogLeNet [54],[20],[21],[67] 4 NASnet [56],[39],[22] 3 ShueNet [84],[20] 2 EcientNet [34] 1 Simple CNN [28] 1 Fig. 13: A bar chart describing the use of di erent CNN models for X-Ray images (by month) 3.3.3 Specialized CNN Methods for COVID-19 Some researchers developed several architectures especially for COVID-19 de- tection with the backbone of basic CNN. These architectures have additional capabilities to classify images into multiple classes like COVID-19, Viral pneu- monia, Bacterial Pneumonia, and Normal case. Because in the primary stage, these models are trained on ImageNet, and then it is trained on various lung 20 Faisal Muhammad Shah et al. diseases CT or X-Ray images. In the following, a brief discussion on the en- semble or specialized CNN methods to detect COVID-19 is described. COVID-19 Detection Neural Network (COVNet) architecture was intro- duced by Li et al. [70] which is a 3D deep learning architecture to detect COVID-19. This architecture can extract both 2D local and 3D global illus- trative features. The COVNet architecture is built with a ResNet architecture as the base model. A max-pooling operation is used for the feature extraction which is carried out for all slices of an image. Moreover, the feature map is connected with a fully connected layer and the author used a softmax acti- vation function for the probability score to accurately classify multiple class (COVID-19, Community-Acquired Pneumonia (CAP), and non-pneumonia). COVID-Net architecture is specially adapted for COVID-19 detection from chest X-Ray images. It has high architectural diversity and selective long-range connectivity. The massive use of a projection-expansion-projection design pat- tern in the COVID-Net [48] architecture is also observed for the classi cation. COVID-Net architecture is incorporated into a heterogeneous association of convolution layers. The proposed COVID-Net is pre-trained on the ImageNet dataset and then applied to the COVIDx dataset. Applying this architecture, they achieved accuracy about 93.3% on the COVIDx dataset. ChexNet is originally a DenseNet-121 type of deep network which is trained on Chest X-ray images introduced by the paper [88]. So, this architecture has been specially designed to diagnose COVID-19.1024-D feature vectors are ex- tracted for the compact classi ers in ChexNet. They used the Softmax activa- tion function to classify COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The number of trainable parameters in this model is 6,955,906. COVID-CAPS is a capsule-based network architecture invented by Afshar et al. [55]. This model consists of four convolutional layers and three capsule layers. The primary layer is a convolutional layer, then batch-normalization is attached. The second layer is also a convolutional layer, followed by a pooling layer. Correspondingly, the third and fourth layers are convolutional, and the fourth layer is reshaped as the rst capsule layer. Three Capsule layers are embedded in the COVID-CAPS to perform the routing. The last Capsule layer contains the classi cation parameters of the two classes of positive and negative COVID-19. The trainable parameter is 295,488 for this model. and achieved 98.3%. Detail-Oriented Capsule Networks (DECAPS) architecture is introduced by Mobiny et al. [18] which uses a ResNet with three residual blocks. This architecture is trained in CT images. This model obtained an area under the curve (AUC) of 98%. Besides these, some papers adopted di erent types of ensemble approaches like Details Relation Extraction neural network (DRE- Net) [60] | ResNet-50 on Feature Pyramid Network [FPN] for extracting top K details from each image. Furthermore, an attention module is combined to learn the importance of every detail. In the training stage, [71] and [10] employed the Least Absolute Shrinkage and Selection Operator (LASSO) to traverse the optimal subset of clinical-radiological features for classi cation. Moreover, GLCM, HOG, and LBP are used by [20]. Additionally, [9] used com- A Comprehensive Survey of COVID-19 Detection using Medical Images 21 mercial o -the-shelf software that detects nodules and small opacities within a 3D lung volume and subsystem. Besides some authors applied transfer learning [83], [62], [ ?], [85] approach with the basic CNN models for better results. Basically, transfer learning is a technique for foretelling modeling on a di erent but somehow the same problem that can then be reused partially or fully to expedite the training and develop the performance of a model on the problem. In deep learning, transfer learning means regenerating the weights in one or more layers from a pre- trained network architecture in a new model and either keeping the weights xed, ne-tuning them, or adapting the weights entirely when training the model. 3.4 Interpretability Fundamentally, a learning model consists of algorithms that try to learn pat- terns and relationships from the data source. To make the results obtained from machines interpretable, researchers use di erent techniques such as Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM) based on a heatmap. 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 visualiza- tion that can be interpreted as where in the image the neural net is searching to make its decision. This is very important in image classi cation and object localization problems. In our survey, there are few papers that utilized CAM [101] and few papers [70], [9], [19], [11], [83], [62], [53], [33], [35], [36], [37], [38], [39], [63], [44] utilized Grad-CAM with heatmap for better understanding of the region it is centering on. At the same time, heatmaps can also provide the radiologists with more useful information and further help them. 3.5 Classi cation Almost all of the COVID-19 diagnosis models use Convolutional Neural Net- work [96] as a feature extractor and as a classi er, it uses softmax or sigmoid. Some authors also attempted to amplify CNN with a sigmoid layer. The au- thors of [45] merged CNN with the softmax layer along with the SVM classi er [102]. SH et al. [56] used CNN with softmax layer along a decision tree, random forest, XGBoost [103], AdaBoost [104], Bagging Classi er [105] and LightGBM [106]. Furthermore, the authors of [88] also merged CNN with KNN, support estimator network, and SVM classi er. Nonetheless, these models need a large amount of data for training which is in shortage of COVID-19 images. Essentially there are two ways of classifying COVID-19 images, Binary Classi cation, and Multiclass classi cation. In Binary Classi cation authors 22 Faisal Muhammad Shah et al. tried to separate COVID-19 and non-COVID-19 patients, but this technique is very inaccurate as other types of lung diseases (viral pneumonia, viral pneu- monia, bacterial pneumonia, and Community-Acquired Pneumonia) can be classi ed as COVID-19. For that reason, many authors di erentiate COVID- 19, viral pneumonia, bacterial pneumonia, community-Acquired Pneumonia, and normal images by classifying them using a softmax classi er. In terms of accuracy of detecting COVID-19 images, multiclass classi ers performed better than binary classi ers. A summary of di erent classi cation techniques used by di erent papers is illustrated in Table 8 and Table 9. Table 8: Summary of classi cation methods used by di erent papers both for CT and X-Ray images Classi cation Methods Papers Count Binary Classi cation[71], [9], [24], [25], [64], [73], [46], [28], [54], [12], [29], [13], [30], [84], [74], [31], [75], [55], [76], [18], [85], [78], [20], [56], [79], [53], [35], [38], [80], [87], [81], [88], [82], [39], [67], [68], [89], [22], [45], [41], [43], [58], [44]43 Multiclass Classi cation[3], [60], [69], [70], [72], [59], [25], [26], [19], [46], [61], [47], [51], [11], [83], [13], [14], [62], [76], [32], [78], [33], [34], [36], [86], [37], [50], [48], [65], [21], [66], [81], [10], [39], [57], [49], [42]37 Table 9: Summary of classi cation methods used by di erent papers both for CT and X-Ray images (month-wise) Month Binary Count Multiclass Count February, 2020 | 0 [3], [60] 2 March, 2020[5], [9], [24], [25], [64], [73], [46], [28]8[69], [70], [72], [59], [25], [26], [19], [46], [61], [47]10 April, 2020[54], [12], [29], [13], [30], [84], [74], [31], [75], [55], [76], [18], [85], [78], [20], [56], [79], [35]19[51], [11], [83], [13], [14], [62], [76], [32], [78] [33], [34], [36], [86]13 May, 2020 [38], [80], [87], [81] 4[37], [50], [48], [65], [21], [66], [81]7 June, 2020[88], [82], [39], [67], [68], [89], [22], [45], [41], [43], [58], [44]12 [10], [39], [57], [49], [42] 5 Some authors also tried to detect COVID-19 in several stages. In the begin- ning, the authors separated normal and pneumonia images. After that, they classify COVID-19 by ltered pneumonia images. Several-stage classi cation helps the models to memorize various leveled connections. In paper [35] and [37], authors used several-stage classi cation rather than an end to end method A Comprehensive Survey of COVID-19 Detection using Medical Images 23 to detect COVID-19 which outperforms several the end to end techniques. On the ip side, the performance of multiclass classi cation relies on datasets. If there is a shortage of dataset, the model cannot become familiar with the various leveled connections between classi cations like Pneumonia to Viral Pneumonia to COVID-19. 3.6 Experimental results of the papers Researchers used di erent evaluation metrics to analyze their COVID-19 model's performance. Among them, the most popular and used metrics for detecting COVID-19 are Accuracy, Precision, Recall/Sensitivity, F1 Score, Speci city, and Area Under Curve (AUC). In our work, we tried to record the perfor- mance with these metrics from all the papers which is represented in Table 10 for CT and in Table 11 for X-Ray images. Also, we have given the number of COVID-19 images from the total images used for training, testing, and valida- tion purpose. Some papers explicitly stated the train-test split of COVID-19 images and for some papers, we calculated the split according to the ratio that is provided in the paper. Even so, for some papers, it is not clearly stated how they distributed their dataset ([71], [13], [14], [20], [17]). Additionally, some papers explicitly stated the use of data for validation ([60], [70], [59], [24], [64], [46], [61], [29], [30], [84], [78], [79], [53], [35], [86], [37], [38], [66], [81], [41], [49]). A summary of the results obtained by the studied models using CT images is illustrated in Table 10. These papers with their Accuracy, AUC, Sensitivity, and Speci city are given along with their distribution of COVID-19 images in training, testing, and validation set. It can also be observed that CT image- based models gained a minimum accuracy of 79.50% for the paper [82] and maximum accuracy of 99.56% for the paper [13]. Table 10: Summary of result evaluation for CT images Publication Image TrainingTesting+ ValidationResult(%) Citation February 21, 2020 [3] 618 189 30 + N/A Accuracy:86.70 117 February 25, 2020 [60]274 79 25 + 9Accuracy:94.00 AUC:91.0022 March 01, 2020 [69] 46096 691 636 + N/AAccuracy:98.85 Sensitivity:94.34 Speci city:99.1637 March 19, 2020 [70] 4356 1048 131 + 117Sensitivity:90.00 Speci city:96.16 AUC:96.00172 March 22, 2020 [71] 2685 NS NS + NSAccuracy:87.90 Sensitivity:90.70 Speci city:83.30 AUC:94.2024 March 23, 2020 [72] 1418 723 154 + N/ASensitivity:97.4 Speci city:92.2 AUC:99.1013 March 24, 2020 [9] 206 50 56 + N/ASensitivity:98.20 Speci city:92.20 AUC:99.6087 24 Faisal Muhammad Shah et al. Publication Image TrainingTesting+ ValidationResult(%) Citation March 26, 2020 [64] 5372 1266 102 + 92Accuracy:85.00 Sensitivity:79.35 Speci city:71.43 AUC:86.008 March 26, 2020 [73] 630 289 76 + N/AAccuracy:90.10 Sensitivity:90.70 Speci city:91.1035 March 30, 2020 [61] 89628 7543 4887 + 2514Accuracy:98.80 Sensitivity: 98.20 Speci city: 98.902 March 31, 2020 [28] 361 339 17 + N/AAccuracy:94.10 Sensitivity:90.00 Speci city:100.0011 April 06, 2020 [11] 2064 829 109 + N/ASensitivity:94.00 Speci city:98.00 AUC:99.4011 April 07, 2020 [12] 12000 4500 1500 + N/AAccuracy:98.93 Sensitivity:97.60 Speci city:97.635 April 10, 2020 [13] 420 375 45 + N/A Accuracy:99.56 2 April 12, 2020 [14] 110 NS NS + 10-Fold Accuracy:89.00 7 April 14, 2020 [74] 360 120 30 + N/AAccuracy:89.20 Sensitivity:88.60 Speci city:87.60 AUC:92.304 April 15, 2020 [75] 79396 2794 64711 + N/ASensitivity:95.00 Speci city: 93.0011 April 17, 2020 [76] 746 191 98 + 60Accuracy:83.00 AUC:87.004 April 17, 2020 [77] 470 165 110 + N/AAccuracy:93.65 Sensitivity:94.25 Speci city:92.791 April 17, 2020 [18] 746 286 105 + N/AAccuracy:87.60 Speci city:85.20 AUC:96.102 April 21, 2020 [15] 829 298 75 + NSSensitivity:86.70 Speci city:99.301 April 21, 2020 [78] 1044 349 50 + 50Accuracy:86.00 Sensitivity:94.00 Speci city:94.00 AUC:93.003 April 21, 2020 [16] 100 60 40 + N/AAccuracy:83.62 Sensitivity:97.423 April 24, 2020 [79] 1065 340 N/A + 290Accuracy:89.50 Sensitivity: 87.00 Speci city:88.0077 April 24, 2020 [53] 783 251 108 + 80Accuracy:83.00 Sensitivity:85.00 Speci city:81.732 May 07, 2020 [80] 2522 1196 299 +5 foldAccuracy:97.79 Sensitivity:93.05 Speci city:89.95 AUC:96.351 May 21, 2020 [17] 130 NS NS + NSSensitivity:72.50 Speci city:96.0010 May 28, 2020 [81] 746 178 95 + 76Accuracy:99.40 Sensitivity:100.00 Speci city:98.60 AUC:99.650 June 02, 2020 [10] 4260 751 NS + NSSensitivity:90.19 Speci city:95.76 AUC:97.1712 June 17, 2020 [82] 812 349 N/A + N/AAccuracy:79.50 AUC:90.1035 A Comprehensive Survey of COVID-19 Detection using Medical Images 25 Publication Image TrainingTesting+ ValidationResult(%) Citation Total = 30Average Accuracy: 90.69 Sensitivity:91.48 Speci city:92.26 AUC: 94.46 A summary of the results obtained by the studied models using X-Ray im- ages is illustrated in Table 11. Papers with their Accuracy, AUC, Sensitivity, and Speci city are given along with their distribution of COVID-19 images in training, testing, and validation set. It can also be seen that X-Ray image- based models gained a minimum accuracy of 89.82% for the paper [19] and maximum accuracy of 99.94% for the paper [13]. For both Table 10 and Table 11, the publication date, the total number of images used by the respective pa- pers is provided. Also, cited by (Number of papers) indicates the total number of papers that have cited the speci c paper up to July 10, 2020. Table 11: Summary of result evaluation for X-Ray images Publication Image TrainingTesting+ ValidationResult(%) Citation March 24, 2020 [59] 50 10 5 + 10Accuracy: 90.00 Sensitivity:100.00 Precision:100.00 F1 Score: 91.0 AUC:90.0038 March 24, 2020 [24] 100 40 10 + 8Accuracy:98.00 Sensitivity:96.00 Speci city:100.00 Precision:100.00 F1 Score:98.0093 March 25, 2020 [25] 1427 202 22 + N/AAccuracy:96.78 Sensitivity:98.66 Speci city:96.4686 March 27, 2020 [26] 1531 70 30 + N/AAccuracy:96.00 AUC:95.1835 March 27, 2020 [19] 5941 54 14 + N/A Accuracy:89.82 19 March 29, 2020 [46] 3487 304 85 +34 Accuracy:99.94 25 March 30, 2020 [27] 40 N/A N/A + N/AAccuracy:97.48 Sensitivity: 95.27 Speci city:99.705 March 31, 2020 [28] 170 120 50 + N/AAccuracy:98.00 Sensitivity:100.00 Speci city:96.0011 March 31, 2020 [47] 13975 N/A N/A + N/AAccuracy:96.23 Precision:100.00 F1 Score:100.0025 April 01, 2020 [51] 3905 409 46 + N/AAccuracy:99.18 Sensitivity:97.36 Speci city:99.429 April 02, 2020 [54] 5863 N/A N/A + N/AAccuracy:99.00 Precision:98.97 Sensitivity:98.97 F1 Score:98.979 April 09, 2020 [83] 16995 181 78 + N/AAccuracy:91.60 Sensitivity:92.45 Speci city:96.126 26 Faisal Muhammad Shah et al. Publication Image TrainingTesting+ ValidationResult(%) Citation April 09, 2020 [29] 414 146 31 + 30 Accuracy:98.00 1 April 10, 2020 [13] 5216 NS NS + NS Accuracy:94.52 2 April 13, 2020 [30] 455 102 33 + 36Accuracy:91.24 AUC:94.0012 April 13, 2020 [84] 537 125 36 + 18Accuracy:93.5 AUC:94.006 April 14, 2020 [31] 1300 N/A N/A + N/AAccuracy:89.60 Precision:93.00 Sensitivity:98.208 April 16, 2020 [62] 16700 286 27 + N/AAccuracy:99.01 AUC:99.724 April 16, 2020 [55] 864 70 50 + N/AAccuracy:95.70 Sensitivity:90.00 Speci city:95.80 AUC:97.0019 April 17,2020 [32] 15085 149 31 + N/AAccuracy:99.56 Sensitivity:80.533 April 20,2020 [85] 5071 31 40 + N/ASensitivity:97.50 Speci city:95.00 AUC:99.608 April 22,2020 [20] 381 NS NS + NSAccuracy:95.33 Sensitivity:95.33 F1 Score:95.3443 April 22,2020 [56] 274 137 NS + N/A Accuracy:99.00 3 April 24,2020 [33] 109203 180 45 + N/A Accuracy:95.30 1 April 28,2020 [34] 13800 152 31 + N/A Accuracy:93.90 5 April 30,2020 [35] 59937 89 35 + 43 AUC:88.04 2 April 30,2020 [36] 11663 258 60 + N/APrecision:98.15 Sensitivity:88.33 AUC:98.50 F1 Score:92.981 April 30,2020 [86] 15111 175 20 +20 Accuracy:89.40 0 May 01,2020 [37] 6297 105 10 + 10 Accuracy:97.10 1 May 05,2020 [38] 502 126 36 + 18Accuracy:88.90 Speci city:96.40 F1 Score:84.409 May 06,2020 [50] 1144 63 27 + N/A F1 Score: 89.60 8 May 08,2020 [87] 6286 370 92 + 5-foldAccuracy:95.90 Sensitivity:98.50 Speci city:95.703 May 11,2020 [48] 13975 258 100 + N/AAccuracy:93.30 Sensitivity:91.00 F1 Score:98.90128 May 17,2020 [65] 196 74 31 + N/A Accuracy:95.12 18 May 21,2020 [21] 2239 191 48 + N/A Accuracy:97.01 4 May 23,2020 [66] 701 270 85 + 68 Accuracy:97.73 0 June 07,20[88] 5824 4659 1165 + N/AAccuracy:99.49 Sensitivity:99.43 Speci city:99.810 June 18,2020 [39] 30099 88 11 + N/AAccuracy:98.00 AUC:99.002 June 16,2020 [40] 15282 286 100 + N/A Accuracy:98.06 0 June 23,2020 [67] 51960 736 314 + N/AAccuracy:97.54 Sensitivity:97.88 Speci city:97.150 June 23,2020 [68] 2071 350 150 + N/A Accuracy:98.90 0 June 11, 2020 [89] 8474 7626 848 + N/A Accuracy:98.60 0 June 18, 2020 [22] 3300 247 62 + N/ASensitivity:100.00 Speci city:99.500 May June [45] 50 20 5 + N/AAccuracy:91.00 Sensitivity:100.00 Speci city:80.000 A Comprehensive Survey of COVID-19 Detection using Medical Images 27 Publication Image TrainingTesting+ ValidationResult(%) Citation June 19,2020 [41] 1312 228 65 + 33Accuracy:97.40 Sensitivity:85.15 Speci city:99.050 June 09,2020 [57] 6523 N/A N/A + N/AAccuracy:98.00 Sensitivity:96.00 Speci city:98.000 June 20,2020 [63] 305 N/A N/A + N/A Accuracy:97.40 0 June 18,2020 [49] 35500 7340 7340 + 500 Accuracy:98.00 0 June 09,2020 [42] 1262 100 54 + N/ASensitivity:90.74 Speci city:95.571 June 10,2020 [43] 1302 142 94 + N/APrecision:88.90 Sensitivity: 85.100 June 02,2020 [58] 754 302 75 + 5 fold Accuracy:97.37 0 June 08,2020 [44] 1419 106 26 + k foldAccuracy:98.94 Sensitivity:96.000 Total = 52Average Accuracy:96.00 Sensitivity:91.09 Speci city:96.45 AUC:95.50 Comparing Table 10 and Table 11, it can be said that the X-Ray image- based models performed better than the CT image-based models. The average Accuracy, Sensitivity, Speci city, and AUC for CT Image-based models are 90.69 %, 91.48%, 92.26%, and 94.46% respectively and for X-Ray based models are 96.00%, 91.09%, 96.45%, and 95.50% respectively. 4 Conclusion As COVID-19 is spreading worldwide expeditiously, accurate and faster de- tection of the disease has become the most essential objective to defend this outbreak. In this article, we tried to present a comprehensive survey of AI- empowered methods that use medical images to combat this pandemic. The fundamental purpose of this survey is to represent the current information so that researchers understand and aware of the up-to-date knowledge and build a model that can accurately detect COVID-19 disease at an economi- cal cost and relatively faster in time. We surveyed a total of 80 COVID-19 diagnosis architectures among which 28 are using CT images, 50 are using X-Ray images and 2 are using both CT and X-Ray images. Till now none of these models are proved to be as reliable to replace RT-PCR tests and still, researchers are trying to improve these techniques. From our survey, it is no- ticeable that the X-Ray image dataset is more widely available than the CT Image dataset as a CT scan procedure is costlier and more time-consuming than an X-Ray. So, most of the researchers utilized Chest X-ray images for diagnosing COVID-19. After analyzing the existing research works in this do- main, we nd out that there exists a shortage of annotated medical images of COVID-19 a ected people. Enriching quality annotated medical images of 28 Faisal Muhammad Shah et al. COVID-19 a ected people can play a signi cant role to boost up the per- formance of the mentioned data-hungry models. Furthermore, we remarked that using segmentation as preprocessing has an extensive impact on model performance. We also observed that domain adoption in transfer learning is the widely used technique which gives a promising result. Furthermore, many researchers used Gradient-weighted Class Activation Mapping (Grad-CAM) with heatmap to interpret the performance of the model. Though this survey paper cannot claim to be an in-depth think about those studies, it presents a practical outlook and shows a valid comparison of the researches in this eld over these months which can be the conductor for the researcher to nd future direction. 5 Declarations { Funding: This study was not funded by any funding agency or anybody. { Con ict of Interest: All of the Authors declare that he/she has no con- ict of interest. { Availability of data and material: All the data are collected and cited from published sources. { Code availability: This a survey paper. We have not conduct any exper- imental research. { Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.