key: cord-0785301-qntu3jzq authors: Bhattacharya, Sweta; Reddy Maddikunta, Praveen Kumar; Pham, Quoc-Viet; Gadekallu, Thippa Reddy; Krishnan S, Siva Rama; Chowdhary, Chiranji Lal; Alazab, Mamoun; Piran, Md. Jalil title: Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey date: 2020-11-05 journal: Sustain Cities Soc DOI: 10.1016/j.scs.2020.102589 sha: ee8531e9d26032f1beb547df18be0f6732bed663 doc_id: 785301 cord_uid: qntu3jzq Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities. The Coronavirus disease (COVID- 19) pandemic and its related efforts of containment have generated a worldwide health crisis impacting all sectors of human life. At its initial stage of inception, with the number of people affected by the disease being minimal, it did not reflect threats of such enormous capacity 5 wherein the majority of the cases were resolved spontaneously. With gradual progression of time, COVID-19 was declared as an outbreak by the World Health Organization (WHO) with an extremely high-risk potential of affecting millions of lives in all countries, especially ones with weaker health systems. The virus is deadly due to two basic reasons-firstly, it is novel with no vaccines discovered, Transmission Level 2 Transmission Level 1 Transmission Level N (Epidemic) and medical image processing techniques to combat the COVID-19 pandemic presenting an extensive review of the state-of-the-art frameworks developed by employing these technologies. In a desperate attempt to combat the COVID-19 pandemic, researches have 55 been initiated on scientific studies in all directions, and DL integrated with medical image processing techniques have also been explored rigorously to find a definite solution [4, 5] . Numerous research publications have been published with similar objectives, as shown in Table. 1. The uniqueness of the present work lies in its effort to emphasize significant DL and image processing tech- the text contents, mentions that "it is the process of combining and associating information from one or multiple sources to provide useful information for the detection, identification, and characterization of a particular entity". In ML and DL applications, the availability of large-scale, high-quality datasets plays a major role in the accuracy of the results. Information fusion helps to integrate 80 multiple datasets and use them in the DL models to achieve enhanced accuracy in predictions. As an example, computed tomography (CT) images from Xi'an Jiaotong University and Nanchang First Hospital and Xi'an No. 8 Hospital have been integrated as part of Information fusion to be fed into the AI and DL models [6] . Similar information fusion has been observed in [7] where X-ray images 85 of the lung from Dr. Joseph Cohen's GitHub repository have been augmented with Chest X-ray images available from the publicly available Kaggle repository. In [8] , X-ray image datasets from GitHub, Cohen, Radiology Society of North America (RSNA), and Italian Society of Medical and Interventional Radiology (SIRM) were associated and used fed into the CNN for detecting COVID-19. In 90 the later sections of the text, similar references can be visualized pertaining to applications of information fusion in order to fill the lag of data unavailability and still continue to generate predictions of enhanced quality. It is important to understand that the pandemic is at its peak where exist-5 J o u r n a l P r e -p r o o f ing medical facilities are overwhelmed. The emergency departments, intensive 95 care facilities have been stretched beyond their regular capacity to serve the ever-growing population of patients. In such a crisis, the healthcare providers and also the patient family members need to make rapid decisions with minimal information. The phenotype of the COVID-19 disease starts with mild or no symptoms at all, yet rapidly changes its course to making patients extremely where there are shortages of radiologists due to an overwhelming number of patients [10] . After conducting an extensive background study, it is evident that there is not many surveys conducted emphasizing the applications of DL frameworks and image processing in the prediction of COVID-19 cases. The present pandemic situation across the globe has impacted millions of lives. Thousands and 6 J o u r n a l P r e -p r o o f thousands of people are getting affected by this highly contagious disease lead- 125 ing to questions on survival and sustainability of the human race [11] . The only way to contain the disease is to detect the disease at its initiation, barring others from getting infected. This requires accelerated diagnosis without associated health hazards. The traditional approaches fail to provide the same due challenges pertinent to detection time, cleaning needs after each use of the 130 diagnostic machinery and availability of resources. The use of ML approaches eliminates these issues and also detects faster. ML approaches, if used more predominantly, can lead to containment of the disease and reduce mortality. The paper thus provides comprehensive information on various DL implementations in COVID-19 using real-time as well as publicly available image 135 datasets. The unique contributions of our study are mentioned below: • The survey includes basic information on COVID-19 and its spread, which establishes the motivation and need for accelerated disease prediction ensuring containment of the disease in smart cities. • The role of DL applications in medical image processing is discussed in 140 detail in support of its capability in COVID-19 predictions. • The recent works on DL and image processing implementations in COVID-19 are discussed explicitly. • The datasets, methodologies, evaluation metrics, research challenges, and the lessons learned are included from these state-of-the-art research works 145 in addition to the future directions in controlling the pandemic in smart cities. The rest of this work is organized as follows. Section 2 presents fundamental information on COVID-19, DL and expresses the general motivation towards the ing. Section 6 summarizes the aforementioned reviews highlighting the lessons learned and enlisting the recommendations guiding towards the future direction of research. The paper is concluded in Section 7. J o u r n a l P r e -p r o o f This section presents the fundamentals of COVID-19, DL, and an overview 160 of the adoption of DL to process and analyze medical images from the existing literature. At the outset, multiple pneumonia cases were being registered in the Wuhan adapt to multiple data types across different domains. Fig. 2 depicts various techniques used in DL. DL replicates the functioning of human brain in filtering information for accurate decision making. Similar to the human brain, DL 220 trains a system to filter the inputs using different layers to aid the prediction and classification of data. These layers are like layered filters used by the neural networks in the brain where each layer acts as a feedback to the next layer. The feedback cycle continues until the precise output is obtained. The precise output is formed by assigning weights in each layer, and during training, these 225 weights are adjusted to get the accurate output. DL techniques can be categorized as supervised, semi-supervised, and unsupervised. In supervised learning, the model is trained with a known inputoutput pair. Each known value constitutes an input vector and the desired value, which is referred to as the supervisory signal. The method uses existing 230 labels to predict the labels of the desired output. Classification methods use supervised learning [32] and can be applied to scenarios to identify faces, traffic symbols, recognizing spam in a given text, converting speech to text, etc. Semi-supervised learning is an in-between technique of supervised and unsupervised ML methodologies. The training data in Semi-supervised learning 235 consists of labeled and unlabelled values. Semi-supervised learning falls between unsupervised learning and supervised learning. The unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. There exist certain scientific assumptions related to DL techniques [33] . The first being, data in proximity to each other 240 have the same label. Second is the cluster assumption, where the data in the cluster share the same label. The third being, the data is restricted to a limited dimension rather than the complete input space. Unsupervised learning deals with knowing the inter-relations among the elements of the data set and then classifying the data without using labels. Some of the algorithms following these 245 techniques are clustering, anomaly detection, and NN. Clustering is the principle of identifying similar elements or anomalies in a data set [34] . This anomaly detection of unsupervised learning is widely applied in security domains [35] . Most of the DL techniques use Artificial Neural Network (ANN) for feature processing and extraction. Feedback technique is used for the learning mech-250 anism [36] where-in each level updates its input data to form a summarized representation. The term deep in DL technique refers to the number of layers required for the data to be transformed. A Credit Assignment Path (CAP) is used during this transformation process. In the case of a feed-forward NN, the depth of CAP is calculated by the number of hidden layers in addition to the 255 number of output layers. In the case of a Recurrent Neural Network (RNN), there might be more than one signal which traverses multiple times in a layer, and thus the CAP depth cannot be determined [37] . One of the predominantly used techniques of NN for image processing is CNN [38, 39, 40] . In CNN, the feature extraction technique 260 is automated and is performed during the training on the images making DL the most accurate method for image processing domains. RNN works similar to CNN, but the difference is that RNN is used for language computation. RNN uses the concept of feedback loops where the output of one layer is fed as the input of the next layer. RNN can be used for datasets which involve time-series 265 [41] , text, financial data, audio, video, etc. Generative Adversarial Networks (GANs) works on the concept of the generator network and the discriminator. The generator network produces fake data while the discriminator differentiates fake and real data. These two networks work towards improvising the training process, and thus GANs are mostly 270 used in an application that requires the generation of images [42] from the text. Google's inception network introduces inception block to compute convolutions and pooling operations that run simultaneously for the effective processing of complex tasks. This is an advanced level of DL used in automating the responsibility involved in image processing [43] . DL can be applied in varied domains, which involve the processing of a vast set of data. DL has great potential in smart cities as a huge amount of data will be generated in smart cities due to digitization [44, 45] . The evaluation of DL techniques relies on two parameters: firstly, the enormous amount of data size to be processed and, secondly, the massive computational power. DL also aids 280 in the faster analysis of complex medical images [42] for rendering an accurate diagnosis. DL is popularly implemented in the health care sector for broad data interpretations [46] , aiding early diagnosis of diseases, thereby reducing manual workload. The following section provides an overview of DL applications for medical image processing. Advances in medical science have significantly changed health care over the last few decades, allowing doctors to identify and treat diseases more effectively [47] . But doctors, similar to any human beings, are also prone to errors. The scholarly credentials of a doctor lie not only in the individual's level of intelli-290 gence, but the way they treat the problems of patients and the associated type improving the strength of a doctor in diagnosing and treating patients [49] . The effectiveness of ML algorithms depends on the types of features extracted and 295 data representation. ML algorithms primarily face two key challenges, one being efficiency in scanning all high-dimensional datasets and secondly training of the model to find the most appropriate task [50, 51] . DL has been one of the commonly used techniques that guarantees a higher degree of accuracy in terms of disease prediction and detection. Applications of DL techniques have intro- [52, 53, 54] . CNN is one of the most preferred algorithm popularly used 305 in image processing and analysis [40] . The authors in [55] reviewed various DL methods for medical image processing and have inferred the use of DL in object identification, image categorization, segmentation, etc. In the medical domain, DL for image processing is used in various departments such as ophthalmology, neurology, psychotherapy, cancer detection, and cardiology. The authors have 310 also enlisted the unresolved research challenges in DL relevant to image analysis. In the current scenario of patients and medical stakeholders maintaining electronic records, AI has aided easing the medical image processing. The authors in [56] reviewed various AI techniques that can be implemented for medical image analysis. The authors from diverse literature found that CNN has been 315 widely used for this analysis, along with big data techniques for processing. The authors also highlighted the main challenges of the unavailability of high quality labeled data for better interpretation. Classification is often termed as Computer-Aided Diagnosis (CAD). Classi-320 fication plays a significant role in medical image processing. During the classification processing phase, one or even more images are taken as input samples, and a single diagnosis factor is generated as an output which classifies the image [57] . In 1995, the authors in [58] used DL to classify lung nodules. The detection procedure involves 55 chest X-ray images, two deep-neural hidden layers. Using 325 this test, the radiologist noticed 82% of lung nodules. In [59], the author's used multi-scale DL approaches to identify lung nodules in CT images. The experimentation process comprises of three hidden layers, which take the CT images as input and provide a response to the output layer of the lung nodule. In [60] , the authors introduced a CheXNet DL model with 121 convolution levels, 1,12,120 330 chest X-ray images provided input dataset to diagnose 14 different forms of lung diseases. Using this examination, the radiologist states the CheXNet algorithm exceeds the range of F1-metric efficiency. In [61] , the authors developed a model by training 10-layer CNN with three completely integrated layers on around 90,000 fundus images to diagnose Diabetic Retinopathy (DR). Experi-335 mental tests attain 95% sensitivity and 75% accuracy on 5,000 testing images. Another related research in [62] employed IDx-DR version X2.1 to train 1.2 million DR images for identifying DR. Results indicate that the built design can achieve a 97% sensitivity and 30% increase in specificity. The work in [63] pro-posed a multi-layer CNN for the classification of skin lesions. The multi-layer 340 CNN is trained with a variety of high-resolution images. Results from the publicly available dataset of skin lesions reveal that the proposed model achieves a better accuracy rate than the other existing models. In the classification, the images are fed to CNN, and the contents of the 345 image are revealed. After the image classification is done, the next step in the detection of the disease is the image localization, which is responsible for placing the bounding box around the output position, which is called as classification with localization, the term localization here refers to figuring out the disease in the image. The localization of anatomy is a crucial pre-processing phase in a 350 clinical diagnosis that enables the radiologist to recognize certain essential features. During recent years, several research works have been conducted using DL models to localize the disease. For example, in [64] , the authors presented a model for the classification of organs or body parts using a deep CNN. The CNN was trained with 4298 X-ray of 1,675 patients to recognize the five organs of The proposed model as a whole is more accurate for the localization of diseases in heterogeneous organs. In [67] , the authors used 3D CNN for landmarking in medical images. Spatial Configuration-Net (SCN) architecture was used to combine accurate response with landmark localization. Experimental evalua-370 tion of 3D image datasets using CNN and the SCN architecture provides higher accuracy. The work in [68] developed a model that helps to localize the fetal in the image. During this process, CNN was trained to recognize up to 12 scan planes and a network model designed to detect fetal accurately. Experimental tests achieved 69% precision, 80% recall, and 81% accuracy. Creating accurate ML models capable of classifying, localizing, and detecting multiple objects in a single image remained a core challenge in computer vision [69] . With recent advancements in DL and computer vision models, medical image detection applications are more comfortable to develop than ever before. Object detection allows for the recognition and localization of multiple objects within an image or video. Object detection is a computer vision technique that is used to identify instances of real-world objects. Object detection techniques train predictive models or use matching templates to locate and identify objects. Object detection algorithms use extracted features and learning algorithms to 385 identify object type instances. Object detection is a key technology behind applications such as video surveillance, image retrieval system, and medical diagnostics [70] . The work in [71] proposed the Marginal Space DL model for object detection. Adaptive training patterns is used for achieving better performance in Deep NN layers. The approximate position, boundary delineation, 390 incorporated with a DL model, find image outline segmentation. The experimental method includes 869 patients, 2891 aortic valve images, delivering 45.2% better performance compared to other previous models. Another work in [72] Another interesting work in [74] proposed a novel lung cancer detection model. This process encompasses two steps. Step one detects dubious pulmonary nodules using a 3-D NN. The second step encompasses cancer detection by collecting the finest five nodules and integration into the leaky noisy-OR Image Segmentation in medical image processing plays a crucial role in dis-415 ease diagnosis. Image segmentation divides a digital image into several fragments. Medical image segmentation aims to make digital images simpler and more comfortable to examine. The output of medical image segmentation is a collection of medical segments covering the whole medical image [77] . Many inter-disciplinary techniques are currently being used for processing medical 420 data for obtaining better accuracy in diagnosis. The authors in [78] propose a Image registration is a method for converting datasets to a single coordinate 455 model. Image registration plays a vital role in the area of medical imaging, biological imaging. Registration is necessary to analyze or integrate data from several medical sources. Usually, a medical technician is supposed to display several images in different directions to reduce the visual contrast between im-19 J o u r n a l P r e -p r o o f ages [82] . The medical technician is often expected to manually classify points 460 in the image that have significant signal variations as part of a sizeable anatomical structure. Medical image registration saves a lot of time for doctors and physicists. To address the shortcoming of the manual registration process, DL implementations in the image registration process have improved the productivity of the image registration process [83] . 465 More than half of cancer patients undergo radiation therapy, making it one of the most prevalent cancer treatments [84] . When the number of patients rises, more doctors and more patients will be assisted by medical image processing. Elastix is fully automated 3D deformable registration software, and its application can enhance the radiotherapy process in radiation oncology departments for the prostate, 93% for the seminal vesicles, and 87% for the lymph nodes. In [85] , the authors proposed a multi-atlas classifier to enhance the accuracy In [86] , the authors used a Self-supervised learning model to establish 3D- Table 2 . This section discusses the potential of DL in medical image processing in order to combat the COVID-19 pandemic implementing four strategies. The strategies are outbreak prediction, the virus spread tracking, coronavirus diagnosis and treatment, vaccination, and drug discovery, as shown in Fig. 4 . X-ray is used to diagnose pneumonia and the basic stage of cancers. But 515 CT scan is a more sophisticated technique that can be used to detect minute changes in the structure of internal organs, and it uses X-ray as well as computer vision technology for its results. X-ray fails to detect diagnosis related of DL based approaches, as discussed in [88] . In CT images, an X-ray rotates and captures images of a particular section, from varied angles. These images are stored in the computer and further analyzed to create a new image that eliminates all overlapping. These images help doctors understand internal structures with enhanced clarity getting the complete idea about size, structure, density, texture, and shape of the same. Thus CT scan is considered to be an effective diagnostic technique than Xray. The chest CT or X-ray fails to differentiate between COVID-19 and other cold-related symptoms. The chest CT or X-ray mostly detects the presence of an infection, which could be the consequence of any other disease as well. Also, the COVID-19 disease is extremely contagious, and the uses of imaging The world faced an unprecedented global health crisis due to the outbreak of COVID-19 [90, 91] . The simple epidemiological and statistical models have attracted considerable attention from the authorities with regard to COVID-19 detection and predictions. It is also a known fact that governments and other technology, and AI (with ML and DL) [95] . It is an established fact that DL has gained immense momentum in the field of ML with its implementations across all sectors of human life [96] . As an example, in the case of data-centric studies such as computer vision, DL methods have proved to be extremely successful in providing optimal solutions [97, 98] . In December 2019, when people were waiting for the New Year celebration of 2020, few cases of typical pneumonia caused by a novel coronavirus (2019-nCoV) [112] were reported in Wuhan, China. The work in [113] revealed that a significant number of people were infected from the wet animal market in Wuhan city, considered the zoonotic origin of the COVID-19. Eventually, multiple 650 cases got spread across China, and the world is giving it the status of a global outbreak [114] . There have been attempts made to identify a host reservoir or intermediate carrier that initiated the spread of COVID-19 from animals to humans [115] . The authors in [116] considered two species of snakes as a possible reservoir of the COVID-19, whereas another study [117] rejected the possibility. information -the number of confirmed infectious cases, death tolls, and recoveries are also available at the Johns Hopkins University dashboard [127] . Later, WHO also launched a COVID-10 dashboard [21] , which operates on ArcGIS. HealthMap [128] is also a dashboard that holds a collection of information from various sources. Aarogya Setu mobile app [129] provides official data of COVID- [130] . A DL-based system was designed to ensure an easy decision for doctors to detect COVID-19 instances of infected pneumonia early enough to control the epidemic. Coronavirus is not a single virus but a group or family of multiple viruses. Once a patient is infected with coronavirus, the symptoms could be similar to normal cold infection or severe respiratory syndromes. As an example, Severe diagnosis is observed to be significantly 80%-90% better than RT-PCR while having 60%-70% specificity on the low side [138] . as an alternative examination [140] . Interestingly, the work in [141, 142] presented a comparison between ultra- diagnosing COVID-19 cases with distinct manifestations. [145] . The work in [146] proposed a detailed guidance report with useful tools to support COVID- Although DL has gained immense momentum, popularity and has generated impressive results with simple 2D images, there exist limitations in achieving a similar level of performance in medical image processing. Research work in this regard is still-in-progress and some of the lessons learned are mentioned below: • One of the most inhibiting factors is the unavailability of large datasets 965 with high-quality images for training. In this case, synthesizing of the data is a possible solution so that the data collected from varied sources could be integrated together • The majority of the state-of-the-art DL models are trained for 2D images. However, CT and MRI are usually 3D and hence add an additional di- implemented on these images • The non-standardized process of collecting image data is one of the major issues in medical image processing. It is important to understand that with 975 the increase in data variety, the need of larger datasets arise to ensure the DL algorithm generates robust solutions. The best possible way to resolve this issue is the application of transfer learning, which makes preprocessing efficient and eliminates scanner and acquisition issues. The challenges and issues pertaining to DL implementations for medical image processing for controlling COVID-19 pandemic in smart cities are enlisted below: • Privacy -Availability of COVID-19 high-quality images and larger datasets is a major challenge considering the privacy of patient data. • Variability in Outbreak Pattern -The outbreak of the data has followed complex pattern and extreme variation in behavior across various countries and hence reliability of the prediction diseases get added as an additional challenge. • Regulation and Transparency -Countries across the globe have adopted 990 strict protocols in regulations to be complied pertinent to sharing of COVID-19 data, one of the major protocols clearly states that minimum data and specimens to be collected from patients in the minimum amount of time. Thus this makes it more difficult to analyze. • Variability in the testing process across various hospitals is also an impor-995 tant concern leading to non-uniformity in data labels. Identification of an appropriate DL technique to exclusively and specifically detect COVID-19 with optimum accuracy still remains as a visible challenge. Moreover, the coronavirus genome has been completely sequenced based on the data collected from thousands of patients suffering from the disease across the globe. This genome sequence has been extremely beneficial, especially due to the fact that the COVID-19 virus has a higher mutation rate. The present diagnostic tests help to identify specific genes from the virus, and the test accu-1005 racy depends on target areas of the relevant genomes. The effect of the mutation on the diagnostic tests is alarming and there exists a high possibility of generating a "false negative" for a patient actually suffering from the disease. These diagnostic tests provide their diagnosis based on the scrutiny of the coronavirus genes which often vary as the disease spreads from one human to another [166] . in the case of transfer learning, the same can be achieved with limited labeled dataset [167] . In this COVID-19 pandemic situation, availability of dataset, furthermore labeled ones, is an obvious challenge and hence transfer learning has immense potential to serve the purpose of COVID-19 detection. As an 1030 example, the study by [168] can be referred to where X-ray and CT images of COVID-19 cases were collected from the GitHub public repository. These where radiology imaging datasets have been found to be prevalent. But utilization of these implementations in real-world medical practice cases is a major concern that dictates the immediate need for benchmarking frameworks for the evaluation and comparison of the existing methodologies. These frameworks should enable the use of computational hardware related infrastructures consid- processing. We believe that the COVID-19 outbreak will be ending soon with 1100 help from DL and image processing techniques as well as many other technologies such as biomedicine, data science, and mobile communications. 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