|Year : 2022 | Volume
| Issue : 3 | Page : 233-253
Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19
Sorayya Rezayi1, Marjan Ghazisaeedi1, Sharareh Rostam Niakan Kalhori1, Soheila Saeedi2
1 Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2 Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran; Clinical Research Development Unit of Farshchian Heart Center, Hamadan University of Medical Sciences, Hamadan, Iran
|Date of Submission||13-Apr-2021|
|Date of Decision||28-Oct-2021|
|Date of Acceptance||03-Jan-2022|
|Date of Web Publication||26-Jul-2022|
Department of Health Information Management, 3rd Floor, School of Allied Medical Sciences, Tehran University of Medical Sciences, No #17, Farredanesh Alley, Ghods St., Enghelab Ave., Postal Code: 14177-44361, Tehran
Source of Support: None, Conflict of Interest: None
Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.
Keywords: 2019-nCoV disease, artificial intelligence, computed tomography, deep learning, image processing, X-ray images
|How to cite this article:|
Rezayi S, Ghazisaeedi M, Kalhori SR, Saeedi S. Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19. J Med Signals Sens 2022;12:233-53
|How to cite this URL:|
Rezayi S, Ghazisaeedi M, Kalhori SR, Saeedi S. Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19. J Med Signals Sens [serial online] 2022 [cited 2022 Aug 13];12:233-53. Available from: https://www.jmssjournal.net/text.asp?2022/12/3/233/351884
| Background|| |
Coronavirus is a family of infectious viruses that can cause diseases typically ranging from the common cold to severe illnesses such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The new coronavirus, called SARS coronavirus 2 (SARS-CoV-2), is the recently known virus in this family that causes COVID-19 disease. The number of patients around the world is increasing dramatically every day and leading to the closure of industries and the quarantine of many people. This disease exerts a severe effect on people's quality of life due to its high transmission power. Considering the pandemic of infectious COVID-19 disease, rapid diagnosis of this disease is enormously essential that can progressively reduce the rate of virus transmission and facilitate the control of the disease. reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard for definitive diagnosis of COVID-19 infection. On the other hand, not all countries have access to these diagnostic kits, and the return time for test results typically varies from 3 h to 48 h. RT-PCR sensitivity may not be high range enough, and its false-negative rate is almost high. According to initial reports, its sensitivity is between 37% and 71%. As a result, patients may be undiagnosed and leading to further spread of the disease.
A familiar way to properly diagnose pneumonia is to employ alternative methods of chest radiography imaging, like X-rays or computed tomography (CT). These imaging techniques are easy to perform and can yield a quick and highly sensitive way to diagnose COVID-19.
Radiographic images are a non-invasive diagnostic way that can identify cases of the disease and help manage and triage the COVID-19 disease. CT scans may indicate similar features between COVID-19 disease and other types of pneumonia, which may prevent a precise diagnosis of COVID-19 disease. On the other hand, it is considered that in many medical centers, radiologists are not available 24 h per typical day.
The practical applications of artificial intelligence (AI) have rapidly entered the clinical field. The increasing complexity and volume of data in healthcare attend an apparent reason why AI techniques will be used in almost every field of medicine in the advancing years. In health care, AI is recommended as an indispensable tool for disease diagnosis and clinical decisions. Deep learning, due to its distinctive characteristics, can provide an opportunity to expand the use of AI-oriented automated techniques in the clinical field. Deep learning represents a subset of machine learning that is becoming a significant and vital technology in the reliable detection and classification of images and video. The use of deep learning in image processing is exceedingly common. Deep learning possesses the considerable potential to facilitate diagnosis from medical images, longitudinal monitoring of disease progression, and determination of disease severity. One of the common algorithms of machine learning comprises convolutional neural networks. Convolutional neural networks are a class of deep learning techniques or deep neural networks chiefly used to analyze visual imagery and classify them. Remarkably, convolutional neural networks have been extremely successful in the classification and detection of medic., However, AI-based methods can help reliably detect COVID-19 from radiology images in real-time with high sensitivity. In this period of the global crisis, it is substantial to accelerate the development of effective AI techniques for diagnosing COVID-19 and its differentiation from pneumonia and other lung diseases in X-ray-oriented images.
Therefore, based on relevant studies, with the outbreak of COVID-19 and the lack of diagnostic kits, many medical centers used radiographic images to diagnose the disease. Simultaneously, many researchers in different countries of the world used automated detection systems based on AI to help accurately diagnose COVID-19 disease with the help of medical images. Various deep learning methods have been used to launch these automated detection tools; each of these methods delivers different accuracy. Graciously according to our best knowledge, there is no comprehensive overview of the methods used in this area to offer the readers an overview in this regard. Therefore, it seems necessary to conduct a study on the use of AI approaches to detect COVID-19 based on radiographic images and CT scans to yield a comprehensive view of this field for researchers.
This study attempts to answer the following questions: (1)Which of the AI and deep learning methods have been used for image processing? (2) Which of the methods have worked best? (3) How accurate was the method used in image processing? (4) Which of the software is most used in image processing? (5) Were most of the images used related to radiology or CT images? (6)What are the sources of the images used in the studies?
| Methods|| |
This systematic review was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMAs) approach which was introduced for the first time by Moher et al.
A systematic and comprehensive search of the scientific database, Medline (through PubMed), Web of Science, Cochrane Central Register of Controlled Trials, IEEE Xplore Digital Library, and Scopus databases was conducted on 21 Sep 2020. The search strategy used in the present review comprised a combination of main keywords from Medical Subject Heading (MeSH) and Emtree (Embase Subject Headings), which were related to “Artificial Intelligence”, “COVID-19 pandemic”, “Diagnostic X-Ray Radiology and X-Ray Computed Tomography.”
Study selection criteria
The following inclusion criteria based on the PICO tool were considered in this systematic review.
- Population: The study population in this systematic review was patients with different COVID 19
- Intervention: Studies that used artificial intelligence techniques were operated for early detection and diagnosis
- Comparison: Not applicable
- Outcome: Articles were included in the review in which intelligent algorithms were applied, and its effectiveness was reported.
The exclusion criteria were the following items:
- Retrieved studies were not about new Coronavirus disease. (2) Articles which were review, book chapters, letters, reports, and technical reports, and (3) non-English published ones, (4) Manuscripts which were in the preprint phase were excluded too.
In our scientific database searching process, 208 papers were retrieved after duplicate removal. Reviewers set some exclusion and inclusion criteria for screening the citations. In the data extraction phase, two independent reviewers (SR and SS) independently determined the main classifications of selected papers and synthesized the key characteristics of selected citations. The key specifications of the papers were validated by MG and S.RNK. Based on the research questions and specific objectives, to select relevant articles, all titles and abstracts were evaluated by two reviewers under the supervision of MG and S.RNK; therefore, the titles and abstracts of the citations were carefully screened by two authors to find relevant papers independently. Another reviewer (MG) randomly reviewed a sample of papers. In total, 86 papers met our inclusion criteria, so they were selected to enter the full-text review phase. The full texts of relevant papers were screened by two reviewers thoroughly (SR and SS). So finally, 60 citations remained as relevant ones; critical characteristics were entered into a spread sheet in Excel in each paper. Two authors (SR and SS) extracted and analyzed the study characteristics independently for each paper based on the predefined classification. For ultimate extraction and for reaching an agreement, the information was re-examined again by two authors. The flow of the screening phase illustrates based on the PRISMA approach in [Figure 1]. The major classifications and items of reviewed citations are displayed in [Table 1].
| Results|| |
Earlier comprehensive searches in scientific databases assigned 290 papers. 208 papers were remained after the duplicate removal phase. In our initial screening phase, 122 articles were eliminated because of their irrelevant titles or abstracts. So, in the last screening step, only 60 studies that met our inclusion criteria were kept. Based on the predefined classification, a summary of the results is described in [Table 1].
Illustration of papers
Of all the studies reviewed, only one was a paper published in a conference, and the rest were published in reputable journals. However, all of the eligible papers which met our inclusion criteria are journal papers. The dating trend of publishing the reviewed articles from the outbreak of COVID-19 up to Sep-2020 is plotted in [Figure 2]. As we can see, the highest number of articles on our topic was published in July.
The distribution of articles based on sample size and data sources of articles
Out of 60 published citations, three papers did not report their applied sample size. The sufficient sample size was varied tremendously from 100 images up to 110,000, and in one paper, the sample size was reported based on recruited cases, not images. The data sources and datasets applied in the reviewed citations were varied considerably. The name of these datasets and their frequency are illustrated in [Figure 3]. It is noteworthy that the most widely used datasets are respectively GitHub repository, hospital-oriented datasets, Kaggle repository, COVID-19 chest CT/X-ray datasets.
The distribution of reviewed citations based on applied software
In this part, we illustrate applied software, technical environments, and tools in reviewed studies. Out of 60 selected papers, 26 citations did not report their applied software and tools, but in the remaining ones, variable tools for deep and machine learning approaches were particularly mentioned. It is remarkable that in the selected studies, several tools have repeatedly been used to conduct research. The reported tools based on their frequency of use are shown in [Figure 4]. As it turns out, tools like The Keras library, Tensorflow, and Python have been in addition widely used in articles.
The distribution of included papers by their publishers
Our selected scientific citations (n = 60) were retrieved from 45 reputable journals and one international conference. The frequency of reviewed papers is displayed in [Table 2]. As it is apparent, “Chaos, Solitons and Fractals,” “Computer Methods and Programs in Biomedicine,” “European Radiology” and “IEEE Journal of Biomedical and Health Informatics” have the highest rank with three papers between the journals.
The distribution of papers by their conducted countries
The selected papers are presented in [Figure 5] based on their conducted countries. As it is clear that 22% of all citations were set in China, and 17% of them were performed in India. In the United States and Saudi Arabia, 16% of the articles were compiled equally.
The distribution of selected articles based on input types
In the reviewed articles, two types of inputs have been utilized to train, test, and validate machine learning techniques and deep neural networks. [Figure 6] shows what kind of images (CT or simple X-ray images) were used in the selected articles. As it turns out, in some cases, both types of images were applied.
The distribution of selected papers based on applied best algorithms
In this section, we examined the best-applied techniques in the reviewed articles. [Figure 7] shows an overview of the distribution of applied image processing methods in reviewed articles. It is apparent that the most favorite method was employed in reviewed articles is combined methods, VGG-19, and VGG-16 networks. Accordingly, such pretrained networks have a high volume of computations but at the same time have better diagnostic accuracy and classification due to their complex structures. In four of the reviewed articles, combined networks have been used to identify and classify images, which adds to the complexity and execution time of the work and highlights the need to provide complex systems.
The distribution of reviewed citations based on their artificial intelligence oriented approaches and reported effectiveness
The effectiveness of applied AI-oriented methods is displayed in [Table 1]. The outstanding results showed that these deep learning and machine learning approaches have the potential and power to early diagnose, detect, and classify COVID-19 disease. The effectiveness and performance of these techniques were reported and assessed by valid criteria such as accuracy, sensitivity, specificity, AUC, F-score, and mean average error. The highest value of accuracy, sensitivity, specificity, and AUC is reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network are equal to one (100%). On the other hand, some of the applied methods provide the most excellent accuracy (100%), like ResExLBP with Iterative ReliefF (IRF) by Support Vector Machine (SVM) classifier and Googlenet. As an accomplished result of the studies, various designed deep convolutional networks such as Mobile Net V2, DenseNet, ResNet, COVID-Xnet, VGG-16, VGG-19, etc., have been used to analyze the chest radiographic images and correctly classify patients with pneumonia and COVID-19. In most studies, feature extraction methods have been properly used to recognize attributes that will be useful in the recognition and categorization of images to optimize learning. However, in some reviewed articles, combined methods, convolutional networks, and supervised machine learning classification models like SVM, DT, KNN, etc., have been utilized likewise. Consequently, it can be considered that the combined use of two numerous approaches of artificial intelligence, i.e., the unique combination of the deep convolution neural networks with classification models, can optimize the approach of accurate patient identification. It is comprehensible that usage of these innovative and efficient methods in medical science, especially in the current coronavirus pandemic, reduces the workload of physicians and medical staff in early diagnosis.
| Discussion|| |
The main objective of our literature review was to critically analyze studies conducted in the chosen field of X-ray-oriented image processing approaches in fierce COVID-19 outbreaks. Hence, broadly 60 citations were selected and reviewed from 208 retrieved irrelevant papers. It is noteworthy that medical images like X-rays and CTs accurately represent a vast source of valuable data in potential patients with COVID-19. In this problematic and challenging situation, medical technologies can effectively use artificial intelligence methods for image processing. The direct results showed that using convolutional deep learning methods for X-rays and CT scans processing can provide an accurate and quick diagnostic tool for unknown Coronavirus disease; the latest deep learning algorithms are currently enabling automated analysis for adequately providing sensitive diagnosis results for this ambiguous unknown disease. These mentioned automated systems are already pervasive in the medical industry, so we expect these intelligent methods are to be able to help in the COVID-19 era and meaningfully improve preliminary treatments and the quality of proper care for patients. Consequently, these AI-based approaches demonstrate considerable potential and analysis benefits to altering the way of specialists work and yielded satisfaction to health organizations and their patients., The results and key findings of our study are eagerly discussed in specific detail in this section.
Due to the favorable review of datasets applied in the selected papers, some main and frequent datasets were widely employed in these studies; these provided datasets in the citations are freely available to the public. For instance, data sources of chest X-rays and CT scans in GitHub repository, Kaggle repository, and NIH Clinical Centers were utilized in a large number of the reviewed articles. These data sources permit researcher teams across diverse countries and around the world to attain them freely, and they promote researchers ’s ability to train computers with intelligent algorithms to diagnose and detect disease properly. By applying the open-access datasets reported in the reviewed studies, researchers hope that research and academic institutions around the world will be able to appropriately train computers and familiar deep learning algorithms can process a significant maximum number of medical images carefully. All the desired results obtained from medical imaging processing (like radiographic X-rays and CTs) can promptly confirm the empirical findings reported by radiologists and potentially ignored and obscure findings can be identified and made available to specialists. It can also be acknowledged that according to our direct results, in a significant number of studies, multinational or local datasets collected from various hospitals in countries and universities have been used. Most local datasets are collected from Chinese hospitals. Besides, 22% of the surveyed studies have been conducted in this country. The reason for this can be attributed to the fact that the first country where the new Coronavirus pandemic appeared was China.
RT-PCR in common is the gold standard to detect the COVID-19, but this standard method contains several limitations, including low sensitivity, lack of diagnostic kit, insufficient laboratory, and time-consuming. Other screening methods that can be used to diagnose this infectious disease are radiographic images such as CT and chest X-rays. According to studies, chest X-rays have been used to image processing more than CT scans. Imaging tools remain rapid screening tools to identify suspected patients promptly. There can be several apparent reasons why CT images have been used sparsely in studies. These include the fact that CT scanners are not widely available. On the other hand, to interpret CT scan images, a radiologist must be involved, which due to the lack of this specialty in medical centers, the use of chest X-rays is more common. In many medical centers, chest X-rays are the first effective tool to diagnose COVID-19. Reasons for the widespread use of this manner to diagnose the disease can be mentioned as follows: Chest X-ray is cheaper than a CT scan, so this factor leads to more use of this method. Also, by imaging with this approach, the patient is exposed to radiation for a shorter time, and for other reasons, we can point out that this method is more rapid.
The results showed that different convolutional neural network techniques were used to process radiographic images (CT and chest X-rays). Our most impressive results showed that utilized AI-based methods had good accuracy. Several contributing factors undoubtedly affect the performance of these systems, including the following: Image content, image quantity, imaging modality, distribution of the dataset, model complexity, the structure of the model, loss function, number of epochs, optimizer, and so on. Various methods can be employed to process images, including VGG series (VGG16, VGG19), Xception, ResNetV1 (ResNet50, ResNet101, ResNet152) and ResNetV2 series (ResNet50V2, ResNet101V2, ResNet152V2), Inception series (InceptionV3, InceptionResNetV2), DenseNet series (DenseNet121, DenseNet169, DenseNet201), and MobileNet.,, VGG16 and VGG19 can provide extremely impressive results in a specific task. ResNetV1 introduced skip reconnections and the Residual layer that can be progressively expanded to hundreds or thousands of active layers in these algorithms. ResNetV2 accepts numerous arrangements in the residual block, and the batch normalization and ReLU activation function are placed before the convolution layer. The InceptionResNetV2 technique is capable to reliably producing higher accuracy at the lower epochs. The Xception represents the expansion of the Inception model, which follows the inception modules with deeply separable convolutions. The MobileNet model is less complex and the size of the model is small. DenseNet series is one of the models that radically reduces the vanishing gradient problem.
Most studies used deep neural networks to analyze images in this review, but these methods vary in simplicity and cost-effectiveness. Artificial intelligence comprises powerful techniques such as VGG 19, DenseNet, VGG 16, ResNet101, and SVM to automate cost estimates with high precision based on collected image data. Nevertheless, the accuracy of cost prediction is a significant criterion in the success of any construction project, where cost overruns are a critical unknown risk, especially with the current emphasis on tight budgets. Applied pre-trained networks are robust and have unique architecture, so it is expected to obtain better results. Also, running time is an important criterion to evaluate neural networks. Pretrained networks often take a long time to run, so they are slow, but our proposed network was fast, and despite its simple architecture, obtained results were promising. However, there is a trade-off between high-performance accuracy and longer execution times, simplicity, and low computational point of view that can be chosen. The studies show that deep learning performance is relatively more when compared with machine learning techniques for extensive data set like images; pretrained models such as MobileNet, MobileNetV2, VGG16, VGG19, and ResNet have been used for image classification and prediction despite their high computational volume and execution time.
This study had several strengths and methodological limitations. Strengths include searching four important databases with comprehensive keywords, which led to the maximum number of accompanying articles and a review of papers presented at the conference. The first restriction of this study was that articles in non-English language are not included. The second limitation of this review was that some conference papers did not have full text and were unincluded in the study. The third limitation of this study was that the performance of applied methods in the various articles was different, making it difficult to compare the performance carefully.
Implications for practice
As a practical plan, considering that some of the designed algorithms, especially convolutional neural networks such as Resnet 18, DenseNet, or Mobile net-V2, maintain extraordinarily high accuracy, sensitivity and specificity settled that the implementation and development of such intelligent techniques in the therapeutic environments can substantially decrease the workload of physicians and radiologists and progressively improve care outcomes. Therefore, most of the networks used in the reviewed studies have high computational dimensions, although for a large volume of data such as medical images are highly useful and can provide optimal diagnostic accuracy.
| Conclusion|| |
The present review analysis can help researchers and health informaticians to properly select the most effective machine learning methods for carefully designing automated COVID-19 disease diagnosis systems. According to the studies reviewed by the research team, this study obtains the first systematic review that examines applied techniques based on artificial intelligence for image processing in the new Coronavirus disease pandemic era. This completed survey revealed that the use of intelligent methods in the field of machine learning could accelerate the process of identifying and diagnosing COVID-19 ambiguous disease, and significant findings extracted from these algorithms can be applied by physicians as an auxiliary diagnostic tool.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Jain G, Mittal D, Thakur D, Mittal MK. A deep learning approach to detect Covid-19 coronavirus with X-ray images. Biocybern Biomed Eng 2020;40:1391-405.
Rahimzadeh M, Attar A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inform Med Unlocked 2020;19:100360.
Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al.
Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology 2020;296:E15-25.
Sekeroglu B, Ozsahin I. <? covid19?> Detection of COVID-19 from Chest X-ray Images Using Convolutional Neural Networks. SLAS TECHNOLOGY: Translating Life Sciences Innovation. 2020;25:553-65.
Li J, Yu X, Hu S, Lin Z, Xiong N, Gao Y. COVID-19 targets the right lung. Crit Care 2020;24:339.
Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, et al
. The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner Society. Chest 2020;158:106-16.
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al.
Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology 2020;296:E32-40.
Ko H, Chung H, Kang WS, Kim KW, Shin Y, Kang SJ, et al.
COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: Model development and validation. J Med Internet Res 2020;22:e19569.
Sakagianni A, Feretzakis G, Kalles D, Koufopoulou C, Kaldis V. Setting up an easy-to-use machine learning pipeline for medical decision support: A case study for COVID-19 diagnosis based on deep learning with CT scans. Stud Health Technol Inform 2020;272:13-6.
Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med 2019;25:44-56.
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv Preprint arXiv: 14091556; 2014.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44.
Zhu J, Shen B, Abbasi A, Hoshmand-Kochi M, Li H, Duong TQ. Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS One 2020;15:e0236621.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2012;25.
Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep Learning for Identifying Metastatic Breast Cancer. arXiv Preprint arXiv: 160605718;2016.
Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, et al.
Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 2016;6:24454.
Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al
. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 2020;E65-E71.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement. PLoS Med 2009;6:e1000097.
Apostolopoulos ID, Aznaouridis SI, Tzani MA. Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. J Med Biol Eng 2020;40:462-9.
Albahli S. A deep neural network to distinguish COVID-19 from other chest diseases using X-ray images. Curr Med Imaging 2021;17:109-19.
Nour M, Cömert Z, Polat K. A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization. Appl Soft Comput 2020;97:106580.
Öztürk Ş, Özkaya U, Barstuğan M. Classification of coronavirus (COVID-19) from X-ray and CT images using shrunken features. Int J Imaging Syst Technol 2020;31:5-15.
Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S, Shukla PK. Deep transfer learning based classification model for COVID-19 disease. Ing Rech Biomed 2020. In press.
Yan T, Wong PK, Ren H, Wang H, Wang J, Li Y. Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos Solitons Fractals 2020;140:110153.
Rajaraman S, Antani S. Weakly labeled data augmentation for deep learning: A study on COVID-19 detection in chest X-rays. Diagnostics (Basel) 2020;10:358.
Cohen JP, Dao L, Roth K, Morrison P, Bengio Y, Abbasi AF, et al.
Predicting COVID-19 pneumonia severity on chest X-ray with deep learning. Cureus 2020;12:e9448.
Dey N, Rajinikanth V, Fong SJ, Kaiser MS, Mahmud M. Social Group Optimization-Assisted Kapur's entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images. Cognit Comput 2020;12:1-13.
Toraman S, Alakus TB, Turkoglu I. Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals 2020;140:110122.
Hassantabar S, Ahmadi M, Sharifi A. Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos Solitons Fractals 2020;140:110170.
Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked 2020;20:100412.
Che Azemin MZ, Hassan R, Mohd Tamrin MI, Md Ali MA. COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: Preliminary findings. Int J Biomed Imaging 2020;2020:8828855.
Bridge J, Meng Y, Zhao Y, Du Y, Zhao M, Sun R, et al.
Introducing the GEV activation function for highly unbalanced data to develop COVID-19 diagnostic models. IEEE J Biomed Health Inform 2020;24:2776-86.
Jaiswal A, Gianchandani N, Singh D, Kumar V, Kaur M. Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. J Biomol Struct Dyn 2021;39:5682-9.
Sun L, Mo Z, Yan F, Xia L, Shan F, Ding Z, et al.
Adaptive feature selection guided deep forest for COVID-19 classification with chest CT. IEEE J Biomed Health Inform 2020;24:2798-805.
Shiri I, Akhavanallaf A, Sanaat A, Salimi Y, Askari D, Mansouri Z, et al
. Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network. Eur Radiol 2020;31:1-12.
Mishra AK, Das SK, Roy P, Bandyopadhyay S. Identifying COVID19 from chest CT images: A deep convolutional neural networks based approach. J Healthc Eng 2020;2020:8843664.
Albahli S. Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia. Int J Med Sci 2020;17:1439-48.
Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med 2020;121:103795.
Duran-Lopez L, Dominguez-Morales JP, Corral-Jaime J, Vicente-Diaz S, Linares-Barranco A. COVID-XNet: A custom deep learning system to diagnose and locate COVID-19 in chest X-ray images. Appl Sci (Switzerland) 2020;10:5683.
Pathak Y, Shukla PK, Arya KV. Deep bidirectional classification model for COVID-19 disease infected patients. IEEE/ACM Trans Comput Biol Bioinform 2020;18:1234-41.
Tuncer T, Dogan S, Ozyurt F. An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based corona detection method using lung X-ray image. Chemometr Intell Lab Syst 2020;203:104054.
Wang Z, Liu Q, Dou Q. Contrastive cross-site learning with redesigned net for COVID-19 CT classification. IEEE J Biomed Health Inform 2020;24:2806-13.
Zamzami N, Koochemeshkian P, Bouguila N, editors. A Distribution-Based Regression for Real-Time COVID-19 Cases Detection from Chest X-ray and CT Images. 2020 IEEE 21st
International Conference on Information Reuse and Integration for Data Science (IRI); 11-13 August, 2020.
Wang S, Zha YF, Li WM, Wu QX, Li XH, Niu M, et al.
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 2020;56:2000775.
Oh Y, Park S, Ye JC. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging 2020;39:2688-700.
Das D, Santosh KC, Pal U. Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys Eng Sci Med 2020;43:915-25.
Mahmud T, Rahman MA, Fattah SA. CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput Biol Med 2020;122:103869.
Abraham B, Nair MS. Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybern Biomed Eng 2020;40:1436-45.
Xu X, Jiang X, Ma C, Du P, Li X, Lv S, et al.
A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering (Beijing, China) 2020;6:1122-9.
Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR, Antani SK. Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-rays. IEEE Access 2020;8:115041-50.
Minaee S, Kafieh R, Sonka M, Yazdani S, Jamalipour Soufi G. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal 2020;65:101794.
Ouyang X, Huo J, Xia L, Shan F, Liu J, Mo Z, et al.
Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging 2020;39:2595-605.
Apostolopoulos ID, Mpesiana TA. Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 2020;43:635-40.
Yang S, Jiang L, Cao Z, Wang L, Cao J, Feng R, et al.
Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: A pilot study. Ann Transl Med 2020;8:450.
Dansana D, Kumar R, Bhattacharjee A, Hemanth DJ, Gupta D, Khanna A, et al.
Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm. Soft Computing 2020. p. 1-9.
Xiao LS, Li P, Sun F, Zhang Y, Xu C, Zhu H, et al.
Development and validation of a deep learning-based model using computed tomography imaging for predicting disease severity of coronavirus disease 2019. Front Bioeng Biotechnol 2020;8:898.
Yoo SH, Geng H, Chiu TL, Yu SK, Cho DC, Heo J, et al.
Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Front Med (Lausanne) 2020;7:427.
Ni Q, Sun ZY, Qi L, Chen W, Yang Y, Wang L, et al.
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images. Eur Radiol 2020;30:6517-27.
Bai HX, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, et al. Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT. Radiology 2020;296:E156-65.
Lessmann N, Sánchez CI, Beenen L, Boulogne LH, Brink M, Calli E, et al.
Automated assessment of CO-RADS and chest CT severity scores in patients with suspected COVID-19 using artificial intelligence. Radiology 2020;1:202439.
Liu C, Wang X, Liu C, Sun Q, Peng W. Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning. Biomed Eng Online 2020;19:66.
Sharma S. Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: A study on 200 patients. Environ Sci Pollut Res Int 2020;27:37155-63.
Singh D, Kumar V, Vaishali, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis 2020;39:1379-89.
Loey M, Smarandache F, Khalifa NE. Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry 2020;12:651.
Vaid S, Kalantar R, Bhandari M. Deep learning COVID-19 detection bias: Accuracy through artificial intelligence. Int Orthop 2020;44:1539-42.
Albahli S, Albattah W. Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms. J Xray Sci Technol 2020;28:841-50.
Rahaman MM, Li C, Yao Y, Kulwa F, Rahman MA, Wang Q, et al.
Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. J Xray Sci Technol 2020;28:821-39.
Pereira RM, Bertolini D, Teixeira LO, Silla CN Jr., Costa YM. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed 2020;194:105532.
Harmon SA, Sanford TH, Xu S, Turkbey EB, Roth H, Xu Z, et al.
Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun 2020;11:4080.
Wu X, Hui H, Niu M, Li L, Wang L, He B, et al.
Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. Eur J Radiol 2020;128:109041.
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020;121:103792.
Khan AI, Shah JL, Bhat MM. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed 2020;196:105581.
Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed 2020;196:105608.
Jalaber C, Lapotre T, Morcet-Delattre T, Ribet F, Jouneau S, Lederlin M. Chest CT in COVID-19 pneumonia: A review of current knowledge. Diagn Interv Imaging 2020;101:431-7.
Manigandan S, Wu MT, Ponnusamy VK, Raghavendra VB, Pugazhendhi A, Brindhadevi K. A systematic review on recent trends in transmission, diagnosis, prevention and imaging features of COVID-19. Process Biochem 2020;98:233-40.
Mertz L. AI-Driven COVID-19 tools to interpret, quantify lung images. IEEE Pulse 2020;11:2-7.
Zhou L, Li Z, Zhou J, Li H, Chen Y, Huang Y, et al
. A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis. IEEE Trans Med Imaging 2020;39:2638-52.
Zhang K, Liu X, Shen J, Li Z, Sang Y, Wu X, et al
. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 2020;181:1423-33.e11.
Kerkech M, Hafiane A, Canals R, Ros F, editors. Vine Disease Detection by Deep Learning Method Combined with 3D Depth Information. International Conference on Image and Signal Processing: Springer; 2020.
Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc: A Dataset for Visual Plant Disease Detection. Proceedings of the 7th
ACM IKDD CoDS and 25th
COMAD; 2020. p. 249-53.
Oakden-Rayner L. Exploring large-scale public medical image datasets. Acad Radiol 2020;27:106-12.
Lu Y, Young S. A survey of public datasets for computer vision tasks in precision agriculture. Comput Electron Agric 2020;178:105760.
Li X, Geng M, Peng Y, Meng L, Lu S. Molecular immune pathogenesis and diagnosis of COVID-19. J Pharm Anal 2020;10:102-8.
Self WH, Courtney DM, McNaughton CD, Wunderink RG, Kline JA. High discordance of chest X-ray and computed tomography for detection of pulmonary opacities in ED patients: Implications for diagnosing pneumonia. Am J Emerg Med 2013;31:401-5.
Zhang W, Li C, Peng G, Chen Y, Zhang Z. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 2018;100:439-53.
Wang J, Deng G, Li W, Chen Y, Gao F, Liu H, et al.
Deep learning for quality assessment of retinal OCT images. Biomed Opt Express 2019;10:6057-72.
Alashhab S, Gallego AJ, Lozano MÁ editors. Hand Gesture Detection with Convolutional Neural Networks. International Symposium on Distributed Computing and Artificial Intelligence, Springer; 2018.
He K, Zhang X, Ren S, Sun J, editors. Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016.
Chollet F, editor Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017.
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al.
Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv Preprint arXiv: 170404861; 2017.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ, editors. Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
[Table 1], [Table 2]