12 results
Search Results
2. Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review
- Author
-
Ashley G. Gillman, Febrio Lunardo, Joseph Prinable, Gregg Belous, Aaron Nicolson, Hang Min, Andrew Terhorst, and Jason A. Dowling
- Subjects
Publishing ,Staging ,Radiological and Ultrasound Technology ,SARS-CoV-2 ,Biomedical Engineering ,Biophysics ,Chest X-ray ,COVID-19 ,Prognosis ,Coronavirus ,Radiography ,COVID-19 Testing ,Artificial Intelligence ,Diagnosis ,Humans ,Radiology, Nuclear Medicine and imaging ,Invited Review Paper ,Instrumentation ,Computed tomography ,Biotechnology - Abstract
Objectives: To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work. Methods: The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test. Findings: Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p
- Published
- 2021
3. Exploring The Application of Artificial Intelligence And Machine Learning To Combat Covid-19 And Implication On Health Services.
- Author
-
Mishra, Nirbhay Kumar, Gandhi, Savleen Singh, and Baba, Misha Hamid
- Subjects
- *
MACHINE learning , *ARTIFICIAL intelligence , *MEDICAL care , *SARS-CoV-2 , *COVID-19 - Abstract
Novel coronavirus (COVID-19) pandemic, has raised a serious situation across world human population and has become serious threat as contagious outbreak. This paper aims to overview the recently intelligent systems based on Artificial Intelligence using different medical imaging modalities like Computer Tomography (CT) and X-ray. This paper specifically discusses the machine learning techniques developed for COVID-19 diagnosis and provides insights on well-known data sets used to train these AI based networks. It also highlights the use of AI in COVID detection and classification at faster process where normal COVID testing takes couple of days to produce. Finally, we conclude by addressing the challenges associated with the use of Machine Leaming methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways machine learning techniques are used and how they potentially further work in com batting the outbreak of CO VID-19. [ABSTRACT FROM AUTHOR]
- Published
- 2022
4. Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review.
- Author
-
Bhargava, Anuja and Bansal, Atul
- Subjects
ARTIFICIAL intelligence ,SARS-CoV-2 ,COMPUTER vision ,COVID-19 ,DIAGNOSIS ,MIDDLE East respiratory syndrome - Abstract
The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using image processing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
5. A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19.
- Author
-
Islam, Md. Mohaimenul, Poly, Tahmina Nasrin, Alsinglawi, Belal, Lin, Ming Chin, Hsu, Min-Huei, Li, Yu-Chuan, and Racanelli, Vito
- Subjects
COVID-19 ,ARTIFICIAL intelligence ,COVID-19 pandemic ,DIAGNOSIS ,DEEP learning - Abstract
Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
6. On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis.
- Author
-
Santone, Antonella, Belfiore, Maria Paola, Mercaldo, Francesco, Varriano, Giulia, Brunese, Luca, and Soilleuxr, Elizabeth
- Subjects
RADIOMICS ,COVID-19 testing ,COVID-19 ,SARS-CoV-2 ,COMPUTED tomography - Abstract
Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
7. Applications of artificial intelligence in battling against covid-19: A literature review.
- Author
-
Tayarani N., Mohammad-H.
- Subjects
- *
ARTIFICIAL intelligence , *COVID-19 , *DIAGNOSIS , *LITERATURE reviews , *COVID-19 treatment - Abstract
• A review on the applications of artificial intelligence on battling against covid-19 is performed. Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
8. A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
- Author
-
Md. Milon Islam, Fakhri Karray, Jia Zeng, and Reda Alhajj
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,General Computer Science ,Coronavirus disease 2019 (COVID-19) ,Computer science ,diagnosis ,Computer Vision and Pattern Recognition (cs.CV) ,Biomedical Engineering ,Computer Science - Computer Vision and Pattern Recognition ,medicine.disease_cause ,Field (computer science) ,030218 nuclear medicine & medical imaging ,Machine Learning (cs.LG) ,03 medical and health sciences ,AI and IoT Convergence for Smart Health ,0302 clinical medicine ,Taxonomy (general) ,Pandemic ,deep transfer learning ,medicine ,FOS: Electrical engineering, electronic engineering, information engineering ,General Materials Science ,Electrical and Electronic Engineering ,Systems, man, and cybernetics ,030304 developmental biology ,Coronavirus ,Computational and artificial intelligence ,0303 health sciences ,business.industry ,Deep learning ,Image and Video Processing (eess.IV) ,General Engineering ,Outbreak ,COVID-19 ,deep learning ,Electrical Engineering and Systems Science - Image and Video Processing ,Data science ,3. Good health ,TK1-9971 ,Categorization ,x-ray ,computer tomography ,Artificial intelligence ,Electrical engineering. Electronics. Nuclear engineering ,business ,Transfer of learning - Abstract
Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19., Comment: 18 pages, 2 figures, 4 Tables
- Published
- 2020
- Full Text
- View/download PDF
9. AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans
- Author
-
Maheshwar Kuchana, Atul Mishra, Amritesh Srivastava, Kiran Khatter, Ronald Das, and Justin Mathew
- Subjects
Coronavirus disease 2019 (COVID-19) ,Computer Networks and Communications ,Computer science ,Pleural effusion ,Chest ct ,Computed tomography ,Article ,Computed Tomography ,Diagnosis ,Media Technology ,medicine ,Segmentation ,Hyperparameters ,Ground Glass Opacities (GGO) ,U-Net architecture ,medicine.diagnostic_test ,Reverse Transcriptase Polymerase Chain Reaction ,business.industry ,Deep learning ,COVID-19 ,Pattern recognition ,Human body ,medicine.disease ,Coronavirus ,Pleural Effusion ,Spatial pyramid pooling ,Hardware and Architecture ,Semantic Segmentation ,Artificial intelligence ,business ,Consolidation ,Software - Abstract
Coronavirus (COVID-19) has spread throughout the world, causing mayhem from January 2020 to this day. Owing to its rapidly spreading existence and high death count, the WHO has classified it as a pandemic. Biomedical engineers, virologists, epidemiologists, and people from other medical fields are working to help contain this epidemic as soon as possible. The virus incubates for five days in the human body and then begins displaying symptoms, in some cases, as late as 27 days. In some instances, CT scan based diagnosis has been found to have better sensitivity than RT-PCR, which is currently the gold standard for COVID-19 diagnosis. Lung conditions relevant to COVID-19 in CT scans are ground-glass opacity (GGO), consolidation, and pleural effusion. In this paper, two segmentation tasks are performed to predict lung spaces (segregated from ribcage and flesh in Chest CT) and COVID-19 anomalies from chest CT scans. A 2D deep learning architecture with U-Net as its backbone is proposed to solve both the segmentation tasks. It is observed that change in hyperparameters such as number of filters in down and up sampling layers, addition of attention gates, addition of spatial pyramid pooling as basic block and maintaining the homogeneity of 32 filters after each down-sampling block resulted in a good performance. The proposed approach is assessed using publically available datasets from GitHub and Kaggle. Model performance is evaluated in terms of F1-Score, Mean intersection over union (Mean IoU). It is noted that the proposed approach results in 97.31% of F1-Score and 84.6% of Mean IoU. The experimental results illustrate that the proposed approach using U-Net architecture as backbone with the changes in hyperparameters shows better results in comparison to existing U-Net architecture and attention U-net architecture. The study also recommends how this methodology can be integrated into the workflow of healthcare systems to help control the spread of COVID-19.
- Published
- 2020
10. On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis
- Author
-
Antonella Santone, Maria Paola Belfiore, Luca Brunese, Giulia Varriano, Francesco Mercaldo, Santone, A., Belfiore, M. P., Mercaldo, F., Varriano, G., and Brunese, L.
- Subjects
Model checking ,formal methods ,Coronavirus disease 2019 (COVID-19) ,Computer science ,diagnosis ,Clinical Biochemistry ,Coronaviru ,Formal method ,Pulmonary disease ,Context (language use) ,Disease ,medicine.disease_cause ,Machine learning ,computer.software_genre ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Radiomics ,medicine ,Coronavirus ,lcsh:R5-920 ,business.industry ,COVID-19 ,Formal methods ,artificial intelligence ,radiology ,radiomics ,HRCT ,CT ,030220 oncology & carcinogenesis ,Artificial intelligence ,business ,lcsh:Medicine (General) ,computer ,Diagnosi - Abstract
Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection, (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection.
- Published
- 2021
11. COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment
- Author
-
Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, and Xiuying Wang
- Subjects
Multitask learning ,Computer tomography ,COVID-19 ,Deep learning ,3D CNNs ,Severity assessment ,Article ,Coronavirus ,Artificial Intelligence ,Signal Processing ,Diagnosis ,Computer Vision and Pattern Recognition ,Software - Abstract
There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.813 ± 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.
- Published
- 2021
12. Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine
- Author
-
Prabira Kumar Sethy, Pradyumna Kumar Ratha, Santi Kumari Behera, and Preesat Biswas
- Subjects
0301 basic medicine ,General Computer Science ,diagnosis ,Local binary patterns ,Computer science ,General Mathematics ,coronavirus ,lcsh:Technology ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,deep features ,0302 clinical medicine ,Contextual image classification ,svm ,lcsh:T ,business.industry ,lcsh:Mathematics ,Deep learning ,General Engineering ,Pattern recognition ,lcsh:QA1-939 ,General Business, Management and Accounting ,Support vector machine ,030104 developmental biology ,Histogram of oriented gradients ,covid-19 ,electrical_electronic_engineering ,Artificial intelligence ,business ,F1 score ,Transfer of learning ,Classifier (UML) - Abstract
The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.
- Published
- 2020
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.