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Wang W, Mao Q, Tian Y, Zhang Y, Xiang Z, Ren L. FMD-UNet: fine-grained feature squeeze and multiscale cascade dilated semantic aggregation dual-decoder UNet for COVID-19 lung infection segmentation from CT images. Biomed Phys Eng Express 2024; 10:055031. [PMID: 39142295 DOI: 10.1088/2057-1976/ad6f12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 08/14/2024] [Indexed: 08/16/2024]
Abstract
With the advancement of computer-aided diagnosis, the automatic segmentation of COVID-19 infection areas holds great promise for assisting in the timely diagnosis and recovery of patients in clinical practice. Currently, methods relying on U-Net face challenges in effectively utilizing fine-grained semantic information from input images and bridging the semantic gap between the encoder and decoder. To address these issues, we propose an FMD-UNet dual-decoder U-Net network for COVID-19 infection segmentation, which integrates a Fine-grained Feature Squeezing (FGFS) decoder and a Multi-scale Dilated Semantic Aggregation (MDSA) decoder. The FGFS decoder produces fine feature maps through the compression of fine-grained features and a weighted attention mechanism, guiding the model to capture detailed semantic information. The MDSA decoder consists of three hierarchical MDSA modules designed for different stages of input information. These modules progressively fuse different scales of dilated convolutions to process the shallow and deep semantic information from the encoder, and use the extracted feature information to bridge the semantic gaps at various stages, this design captures extensive contextual information while decoding and predicting segmentation, thereby suppressing the increase in model parameters. To better validate the robustness and generalizability of the FMD-UNet, we conducted comprehensive performance evaluations and ablation experiments on three public datasets, and achieved leading Dice Similarity Coefficient (DSC) scores of 84.76, 78.56 and 61.99% in COVID-19 infection segmentation, respectively. Compared to previous methods, the FMD-UNet has fewer parameters and shorter inference time, which also demonstrates its competitiveness.
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Affiliation(s)
- Wenfeng Wang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Qi Mao
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Yi Tian
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Yan Zhang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Zhenwu Xiang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
| | - Lijia Ren
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, People's Republic of China
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2
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Arabi H, Zaidi H. Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01159-x. [PMID: 38858260 DOI: 10.1007/s10278-024-01159-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/12/2024]
Abstract
To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation. To this end, three different datasets were employed used for brain tumor and hippocampus delineation from MR images (BraTS and Decathlon datasets, respectively) and kidney segmentation from CT images (Decathlon dataset). Since data augmentation techniques are also aimed at enhancing the performance of deep learning methods, a deformable data augmentation technique was proposed and compared with contrastive learning and self-learning frameworks. The segmentation accuracy for the three datasets was assessed with and without applying data augmentation, contrastive learning, and self-learning to individually investigate the impact of these techniques. The self-learning and deformable data augmentation techniques exhibited comparable performance with Dice indices of 0.913 ± 0.030 and 0.920 ± 0.022 for kidney segmentation, 0.890 ± 0.035 and 0.898 ± 0.027 for hippocampus segmentation, and 0.891 ± 0.045 and 0.897 ± 0.040 for lesion segmentation, respectively. These two approaches significantly outperformed the contrastive learning and the original model with Dice indices of 0.871 ± 0.039 and 0.868 ± 0.042 for kidney segmentation, 0.872 ± 0.045 and 0.865 ± 0.048 for hippocampus segmentation, and 0.870 ± 0.049 and 0.860 ± 0.058 for lesion segmentation, respectively. The combination of self-learning with deformable data augmentation led to a robust segmentation model with no outliers in the outcomes. This work demonstrated the beneficial impact of self-learning and deformable data augmentation on organ and lesion segmentation, where no additional training datasets are needed.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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3
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Chae A, Yao MS, Sagreiya H, Goldberg AD, Chatterjee N, MacLean MT, Duda J, Elahi A, Borthakur A, Ritchie MD, Rader D, Kahn CE, Witschey WR, Gee JC. Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology. Radiology 2024; 310:e223170. [PMID: 38259208 PMCID: PMC10831483 DOI: 10.1148/radiol.223170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 01/24/2024]
Abstract
Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
| | | | - Hersh Sagreiya
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Ari D. Goldberg
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Neil Chatterjee
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Matthew T. MacLean
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Jeffrey Duda
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Ameena Elahi
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Arijitt Borthakur
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Marylyn D. Ritchie
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Daniel Rader
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Charles E. Kahn
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
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4
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Chen F, Chen L, Kong W, Zhang W, Zheng P, Sun L, Zhang D, Liao H. Deep Semi-Supervised Ultrasound Image Segmentation by Using a Shadow Aware Network With Boundary Refinement. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3779-3793. [PMID: 37695964 DOI: 10.1109/tmi.2023.3309249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.
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5
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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6
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PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans. Med Image Anal 2023; 86:102797. [PMID: 36966605 PMCID: PMC10027962 DOI: 10.1016/j.media.2023.102797] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 01/10/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023]
Abstract
Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been widely used to analyse this disease. Indeed, CT-scans of the lungs can help diagnose, detect, and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. To improve the performance of the Att-Unet architecture and maximize the use of the Attention Gate, we propose the PAtt-Unet and DAtt-Unet architectures. PAtt-Unet aims to exploit the input pyramids to preserve the spatial awareness in all of the encoder layers. On the other hand, DAtt-Unet is designed to guide the segmentation of Covid-19 infection inside the lung lobes. We also propose to combine these two architectures into a single one, which we refer to as PDAtt-Unet. To overcome the blurry boundary pixels segmentation of Covid-19 infection, we propose a hybrid loss function. The proposed architectures were tested on four datasets with two evaluation scenarios (intra and cross datasets). Experimental results showed that both PAtt-Unet and DAtt-Unet improve the performance of Att-Unet in segmenting Covid-19 infections. Moreover, the combination architecture PDAtt-Unet led to further improvement. To Compare with other methods, three baseline segmentation architectures (Unet, Unet++, and Att-Unet) and three state-of-the-art architectures (InfNet, SCOATNet, and nCoVSegNet) were tested. The comparison showed the superiority of the proposed PDAtt-Unet trained with the proposed hybrid loss (PDEAtt-Unet) over all other methods. Moreover, PDEAtt-Unet is able to overcome various challenges in segmenting Covid-19 infections in four datasets and two evaluation scenarios.
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Krinski BA, Ruiz DV, Laroca R, Todt E. DACov: a deeper analysis of data augmentation on the computed tomography segmentation problem. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2023.2183807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Affiliation(s)
- Bruno A. Krinski
- Department of Informatics, Federal University of Paraná, Curitiba, Brazil
| | - Daniel V. Ruiz
- Department of Informatics, Federal University of Paraná, Curitiba, Brazil
| | - Rayson Laroca
- Department of Informatics, Federal University of Paraná, Curitiba, Brazil
| | - Eduardo Todt
- Department of Informatics, Federal University of Paraná, Curitiba, Brazil
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Hasan MM, Islam MU, Sadeq MJ, Fung WK, Uddin J. Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. SENSORS (BASEL, SWITZERLAND) 2023; 23:527. [PMID: 36617124 PMCID: PMC9824505 DOI: 10.3390/s23010527] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.
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Affiliation(s)
- Md. Mahadi Hasan
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Muhammad Usama Islam
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Muhammad Jafar Sadeq
- Department of Computer Science and Engineering, Asian University of Bangladesh, Ashulia 1349, Bangladesh
| | - Wai-Keung Fung
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
| | - Jasim Uddin
- Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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9
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Wu S, Nakao M, Imanishi K, Nakamura M, Mizowaki T, Matsuda T. Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase. PLoS One 2022; 17:e0279005. [PMID: 36520814 PMCID: PMC9754169 DOI: 10.1371/journal.pone.0279005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve this problem. However, HRCT suffers from dose increase, and linear interpolation causes artifacts. In this study, we propose a deep-learning-based approach to reconstruct densely sliced CT from sparsely sliced CT data without any dose increase. The proposed method reconstructs CT images from neighboring slices using a U-net architecture. To prevent multiple reconstructed slices from influencing one another, we propose a parallel architecture in which multiple U-net architectures work independently. Moreover, for a specific organ (i.e., the liver), we propose a range-clip technique to improve reconstruction quality, which enhances the learning of CT values within this organ by enlarging the range of the training data. CT data from 130 patients were collected, with 80% used for training and the remaining 20% used for testing. Experiments showed that our parallel U-net architecture reduced the mean absolute error of CT values in the reconstructed slices by 22.05%, and also reduced the incidence of artifacts around the boundaries of target organs, compared with linear interpolation. Further improvements of 15.12%, 11.04%, 10.94%, and 10.63% were achieved for the liver, left kidney, right kidney, and stomach, respectively, using the proposed range-clip algorithm. Also, we compared the proposed architecture with original U-net method, and the experimental results demonstrated the superiority of our approach.
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Affiliation(s)
- Shuqiong Wu
- The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan
- * E-mail:
| | - Megumi Nakao
- Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | | | - Mitsuhiro Nakamura
- Division of Medical Physics, Department of Information Technology and Medical Engineering, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tetsuya Matsuda
- Graduate School of Informatics, Kyoto University, Kyoto, Japan
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10
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Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4431817. [PMID: 36317075 PMCID: PMC9617705 DOI: 10.1155/2022/4431817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/23/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022]
Abstract
During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.
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11
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Shamija Sherryl RMR, Jaya T. Semantic Multiclass Segmentation and Classification of Kidney Lesions. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11034-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
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12
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Zammit J, Fung DLX, Liu Q, Leung CKS, Hu P. Semi-supervised COVID-19 CT image segmentation using deep generative models. BMC Bioinformatics 2022; 23:343. [PMID: 35974325 PMCID: PMC9381397 DOI: 10.1186/s12859-022-04878-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 11/29/2022] Open
Abstract
Background A recurring problem in image segmentation is a lack of labelled data. This problem is especially acute in the segmentation of lung computed tomography (CT) of patients with Coronavirus Disease 2019 (COVID-19). The reason for this is simple: the disease has not been prevalent long enough to generate a great number of labels. Semi-supervised learning promises a way to learn from data that is unlabelled and has seen tremendous advancements in recent years. However, due to the complexity of its label space, those advancements cannot be applied to image segmentation. That being said, it is this same complexity that makes it extremely expensive to obtain pixel-level labels, making semi-supervised learning all the more appealing. This study seeks to bridge this gap by proposing a novel model that utilizes the image segmentation abilities of deep convolution networks and the semi-supervised learning abilities of generative models for chest CT images of patients with the COVID-19. Results We propose a novel generative model called the shared variational autoencoder (SVAE). The SVAE utilizes a five-layer deep hierarchy of latent variables and deep convolutional mappings between them, resulting in a generative model that is well suited for lung CT images. Then, we add a novel component to the final layer of the SVAE which forces the model to reconstruct the input image using a segmentation that must match the ground truth segmentation whenever it is present. We name this final model StitchNet. Conclusion We compare StitchNet to other image segmentation models on a high-quality dataset of CT images from COVID-19 patients. We show that our model has comparable performance to the other segmentation models. We also explore the potential limitations and advantages in our proposed algorithm and propose some potential future research directions for this challenging issue.
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Affiliation(s)
- Judah Zammit
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Daryl L X Fung
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Qian Liu
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.,Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308 - Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, MB, R3E 0J3, Canada
| | - Carson Kai-Sang Leung
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Pingzhao Hu
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada. .,Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308 - Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, MB, R3E 0J3, Canada. .,CancerCare Manitoba Research Institute, Winnipeg, MB, Canada.
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13
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Heidari A, Toumaj S, Navimipour NJ, Unal M. A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain. Comput Biol Med 2022; 145:105461. [PMID: 35366470 PMCID: PMC8958272 DOI: 10.1016/j.compbiomed.2022.105461] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/13/2022] [Accepted: 03/24/2022] [Indexed: 12/16/2022]
Abstract
With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran; Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
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14
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Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106731. [PMID: 35286874 PMCID: PMC8897838 DOI: 10.1016/j.cmpb.2022.106731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.
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Affiliation(s)
- Haseeb Hassan
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
| | - Zhaoyu Ren
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Chengmin Zhou
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Jian Zhao
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Bingding Huang
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
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Gillman AG, Lunardo F, Prinable J, Belous G, Nicolson A, Min H, Terhorst A, Dowling JA. Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review. Phys Eng Sci Med 2022; 45:13-29. [PMID: 34919204 PMCID: PMC8678975 DOI: 10.1007/s13246-021-01093-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/13/2021] [Indexed: 12/31/2022]
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 < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified. INTERPRETATION A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.
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Affiliation(s)
- Ashley G Gillman
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia.
| | - Febrio Lunardo
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
- College of Science and Engineering, James Cook University, Australian Tropical Science Innovation Precinct, Townsville, QLD, 4814, Australia
| | - Joseph Prinable
- ACRF Image X Institute, University of Sydney, Level 2, Biomedical Building (C81), 1 Central Ave, Australian Technology Park, Eveleigh, Sydney, NSW, 2015, Australia
| | - Gregg Belous
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Aaron Nicolson
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Hang Min
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
| | - Andrew Terhorst
- Data61, Commonwealth Scientific and Industrial Research Organisation, College Road, Sandy Bay, Hobart, TAS, 7005, Australia
| | - Jason A Dowling
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Surgical Treatment and Rehabilitation Service, 296 Herston Road, Brisbane, QLD, 4029, Australia
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Awassa L, Jdey I, Dhahri H, Hcini G, Mahmood A, Othman E, Haneef M. Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights. SENSORS (BASEL, SWITZERLAND) 2022; 22:1890. [PMID: 35271037 PMCID: PMC8915023 DOI: 10.3390/s22051890] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.
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Affiliation(s)
- Lamia Awassa
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Imen Jdey
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Habib Dhahri
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Ghazala Hcini
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Awais Mahmood
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Esam Othman
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Muhammad Haneef
- Department of Electrical Engineering, Foundation University Islamabad, Islamabad 44000, Pakistan;
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Abumalloh RA, Nilashi M, Yousoof Ismail M, Alhargan A, Alghamdi A, Alzahrani AO, Saraireh L, Osman R, Asadi S. Medical image processing and COVID-19: A literature review and bibliometric analysis. J Infect Public Health 2022; 15:75-93. [PMID: 34836799 PMCID: PMC8596659 DOI: 10.1016/j.jiph.2021.11.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 01/07/2023] Open
Abstract
COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.
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Affiliation(s)
- Rabab Ali Abumalloh
- Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box. 1982, Dammam, Saudi Arabia
| | - Mehrbakhsh Nilashi
- Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800, USM Penang, Malaysia.
| | | | - Ashwaq Alhargan
- Computer Science Department, College of Computing and Informatics, Saudi Electronic University, Saudi Arabia
| | - Abdullah Alghamdi
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Ahmed Omar Alzahrani
- College of Computer Science and Engineering, University of Jeddah, 21959 Jeddah, Saudi Arabia
| | - Linah Saraireh
- Management Information System Department, College of Business, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia
| | - Reem Osman
- Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box. 1982, Dammam, Saudi Arabia
| | - Shahla Asadi
- Centre of Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
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19
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Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021; 11:diagnostics11101924. [PMID: 34679622 PMCID: PMC8534829 DOI: 10.3390/diagnostics11101924] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022] Open
Abstract
Chest X-rays (CXR) and computed tomography (CT) are the main medical imaging modalities used against the increased worldwide spread of the 2019 coronavirus disease (COVID-19) epidemic. Machine learning (ML) and artificial intelligence (AI) technology, based on medical imaging fully extracting and utilizing the hidden information in massive medical imaging data, have been used in COVID-19 research of disease diagnosis and classification, treatment decision-making, efficacy evaluation, and prognosis prediction. This review article describes the extensive research of medical image-based ML and AI methods in preventing and controlling COVID-19, and summarizes their characteristics, differences, and significance in terms of application direction, image collection, and algorithm improvement, from the perspective of radiologists. The limitations and challenges faced by these systems and technologies, such as generalization and robustness, are discussed to indicate future research directions.
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Affiliation(s)
- Tianming Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Quanliang Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Cong Ma
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Xiangyu Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (T.W.); (Z.C.); (Q.S.); (C.M.); (X.C.)
- Molecular Imaging Research Center, Central South University, Changsha 410008, China
- Correspondence:
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