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Schaffert D, Bibi I, Blauth M, Lull C, von Ahnen JA, Gross G, Schulze-Hagen T, Knitza J, Kuhn S, Benecke J, Schmieder A, Leipe J, Olsavszky V. Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study. JMIR Form Res 2024; 8:e55855. [PMID: 38738977 PMCID: PMC11240079 DOI: 10.2196/55855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/27/2024] [Accepted: 04/11/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data. OBJECTIVE This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score. METHODS Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest. RESULTS Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve [AUC] of 0.9078 and logarithmic loss [LogLoss] of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores. CONCLUSIONS The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.
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Affiliation(s)
- Daniel Schaffert
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Igor Bibi
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Mara Blauth
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Christian Lull
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Jan Alwin von Ahnen
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Georg Gross
- Department of Medicine V, Division of Rheumatology, University Medical Center and Medical Faculty Mannheim, Mannheim, Germany
| | - Theresa Schulze-Hagen
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Johannes Knitza
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Sebastian Kuhn
- Institute of Digital Medicine, Philipps-University Marburg and University Hospital of Giessen and Marburg, Marburg, Germany
| | - Johannes Benecke
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
| | - Astrid Schmieder
- Department of Dermatology, Venereology, and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Jan Leipe
- Department of Medicine V, Division of Rheumatology, University Medical Center and Medical Faculty Mannheim, Mannheim, Germany
| | - Victor Olsavszky
- Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, and Center of Excellence in Dermatology, Mannheim, Germany
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2
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El Houby EMF. COVID‑19 detection from chest X-ray images using transfer learning. Sci Rep 2024; 14:11639. [PMID: 38773161 PMCID: PMC11109273 DOI: 10.1038/s41598-024-61693-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/08/2024] [Indexed: 05/23/2024] Open
Abstract
COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.
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Affiliation(s)
- Enas M F El Houby
- Systems and Information Department, National Research Centre, Dokki, 12311, Cairo, Egypt.
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3
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Yoon T, Kang D. Enhancing pediatric pneumonia diagnosis through masked autoencoders. Sci Rep 2024; 14:6150. [PMID: 38480869 PMCID: PMC10937919 DOI: 10.1038/s41598-024-56819-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 03/11/2024] [Indexed: 03/17/2024] Open
Abstract
Pneumonia, an inflammatory lung condition primarily triggered by bacteria, viruses, or fungi, presents distinctive challenges in pediatric cases due to the unique characteristics of the respiratory system and the potential for rapid deterioration. Timely diagnosis is crucial, particularly in children under 5, who have immature immune systems, making them more susceptible to pneumonia. While chest X-rays are indispensable for diagnosis, challenges arise from subtle radiographic findings, varied clinical presentations, and the subjectivity of interpretations, especially in pediatric cases. Deep learning, particularly transfer learning, has shown promise in improving pneumonia diagnosis by leveraging large labeled datasets. However, the scarcity of labeled data for pediatric chest X-rays presents a hurdle in effective model training. To address this challenge, we explore the potential of self-supervised learning, focusing on the Masked Autoencoder (MAE). By pretraining the MAE model on adult chest X-ray images and fine-tuning the pretrained model on a pediatric pneumonia chest X-ray dataset, we aim to overcome data scarcity issues and enhance diagnostic accuracy for pediatric pneumonia. The proposed approach demonstrated competitive performance an AUC of 0.996 and an accuracy of 95.89% in distinguishing between normal and pneumonia. Additionally, the approach exhibited high AUC values (normal: 0.997, bacterial pneumonia: 0.983, viral pneumonia: 0.956) and an accuracy of 93.86% in classifying normal, bacterial pneumonia, and viral pneumonia. This study also investigated the impact of different masking ratios during pretraining and explored the labeled data efficiency of the MAE model, presenting enhanced diagnostic capabilities for pediatric pneumonia.
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Affiliation(s)
- Taeyoung Yoon
- Department of Healthcare Information Technology, Inje University, 197, Inje-ro, Gimhae-si, 50834, Korea
| | - Daesung Kang
- Department of Healthcare Information Technology, Inje University, 197, Inje-ro, Gimhae-si, 50834, Korea.
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4
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Ameri A, Ameri A, Salmanizadeh F, Bahaadinbeigy K. Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods. Health Sci Rep 2024; 7:e1919. [PMID: 38384976 PMCID: PMC10879639 DOI: 10.1002/hsr2.1919] [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: 04/25/2023] [Revised: 01/30/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
Background and Aims Due to the COVID-19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID-19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID-19. Methods We searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID-19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher. Results A search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge-based CDSS (n = 52) and knowledge-based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS. Conclusion CDSS for COVID-19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision-making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge-based and knowledge-based CDSS in COVID-19 diagnosis.
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Affiliation(s)
- Arefeh Ameri
- Health Information Sciences Department, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Atefeh Ameri
- Pharmaceutical Sciences and Cosmetic Products Research CenterKerman University of Medical SciencesKermanIran
| | - Farzad Salmanizadeh
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Kambiz Bahaadinbeigy
- Digital Health TeamAustralian College of Rural and Remote MedicineBrisbaneQueenslandAustralia
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5
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Singh S, Kumar M, Kumar A, Verma BK, Abhishek K, Selvarajan S. Efficient pneumonia detection using Vision Transformers on chest X-rays. Sci Rep 2024; 14:2487. [PMID: 38291130 PMCID: PMC10827725 DOI: 10.1038/s41598-024-52703-2] [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: 06/20/2023] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.
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Affiliation(s)
| | - Manoj Kumar
- JSS Academy of Technical Education, Noida, India
| | - Abhay Kumar
- National Institute of Technology Patna, Patna, India
| | | | | | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
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Hussein AM, Sharifai AG, Alia OM, Abualigah L, Almotairi KH, Abujayyab SKM, Gandomi AH. Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs. Sci Rep 2024; 14:534. [PMID: 38177156 PMCID: PMC10766625 DOI: 10.1038/s41598-023-47038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/08/2023] [Indexed: 01/06/2024] Open
Abstract
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
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Affiliation(s)
- Ahmad MohdAziz Hussein
- Department of Computer Science, Faculty of Information Technology, Middle East University, Amman, Jordan.
| | - Abdulrauf Garba Sharifai
- Department of Computer Sciences, Yusuf Maitama Sule University, Kofar Nassarawa, Kano, 700222, Nigeria
| | - Osama Moh'd Alia
- Department of Computer Science, Faculty of Computes and Information Technology, University of Tabuk, 71491, Tabuk, Saudi Arabia
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
- School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia
- School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Khaled H Almotairi
- Computer Engineering Department, Computer and Information Systems College, Umm Al-Qura University, 21955, Makkah, Saudi Arabia
| | | | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary.
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Moitra M, Alafeef M, Narasimhan A, Kakaria V, Moitra P, Pan D. Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images. PLoS One 2023; 18:e0290494. [PMID: 38096254 PMCID: PMC10721010 DOI: 10.1371/journal.pone.0290494] [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: 05/10/2023] [Accepted: 08/09/2023] [Indexed: 12/17/2023] Open
Abstract
COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. However, differentiating COVID-19 from other types of pneumonia and predicting associated cardiovascular complications from the same chest-CT images have remained challenging. In this study, we have first used transfer learning method to distinguish COVID-19 from other pneumonia and healthy cases with 99.2% accuracy. Next, we have developed another CNN-based deep learning approach to automatically predict the risk of cardiovascular disease (CVD) in COVID-19 patients compared to the normal subjects with 97.97% accuracy. Our model was further validated against cardiac CT-based markers including cardiac thoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), and presence of calcified plaque. Thus, we successfully demonstrate that CT-based deep learning algorithms can be employed as a dual screening diagnostic tool to diagnose COVID-19 and differentiate it from other pneumonia, and also predicts CVD risk associated with COVID-19 infection.
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Affiliation(s)
- Moumita Moitra
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
| | - Maha Alafeef
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
- Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
- Department of Nuclear Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Arjun Narasimhan
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
| | - Vikram Kakaria
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
| | - Parikshit Moitra
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Nuclear Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Dipanjan Pan
- Center for Blood Oxygen Transport and Hemostasis, Department of Pediatrics, University of Maryland Baltimore School of Medicine, Baltimore, Maryland, United States of America
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, Baltimore, Maryland, United States of America
- Department of Nuclear Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
- Department of Materials Science & Engineering, The Pennsylvania State University, State College, Pennsylvania, United States of America
- Huck Institutes of the Life Sciences, State College, Pennsylvania, United States of America
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8
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Wang F, Li X, Wen R, Luo H, Liu D, Qi S, Jing Y, Wang P, Deng G, Huang C, Du T, Wang L, Liang H, Wang J, Liu C. Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography. Eur Radiol 2023; 33:8869-8878. [PMID: 37389609 DOI: 10.1007/s00330-023-09833-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVES This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification of bacterial, fungal, and viral pneumonia. METHODS A total of 2763 participants with chest CT images and definite pathogen diagnosis were included to train and validate an algorithm. Pneumonia-Plus was prospectively tested on a nonoverlapping dataset of 173 patients. The algorithm's performance in classifying three types of pneumonia was compared to that of three radiologists using the McNemar test to verify its clinical usefulness. RESULTS Among the 173 patients, area under the curve (AUC) values for viral, fungal, and bacterial pneumonia were 0.816, 0.715, and 0.934, respectively. Viral pneumonia was accurately classified with sensitivity, specificity, and accuracy of 0.847, 0.919, and 0.873. Three radiologists also showed good consistency with Pneumonia-Plus. The AUC values of bacterial, fungal, and viral pneumonia were 0.480, 0.541, and 0.580 (radiologist 1: 3-year experience); 0.637, 0.693, and 0.730 (radiologist 2: 7-year experience); and 0.734, 0.757, and 0.847 (radiologist 3: 12-year experience), respectively. The McNemar test results for sensitivity showed that the diagnostic performance of the algorithm was significantly better than that of radiologist 1 and radiologist 2 (p < 0.05) in differentiating bacterial and viral pneumonia. Radiologist 3 had a higher diagnostic accuracy than the algorithm. CONCLUSIONS The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist and reduce the risk of misdiagnosis. The Pneumonia-Plus is important for appropriate treatment and avoiding the use of unnecessary antibiotics, and provide timely information to guide clinical decision-making and improve patient outcomes. CLINICAL RELEVANCE STATEMENT Pneumonia-Plus algorithm could assist in the accurate classification of pneumonia based on CT images, which has great clinical value in avoiding the use of unnecessary antibiotics, and providing timely information to guide clinical decision-making and improve patient outcomes. KEY POINTS • The Pneumonia-Plus algorithm trained from data collected from multiple centers can accurately identify bacterial, fungal, and viral pneumonia. • The Pneumonia-Plus algorithm was found to have better sensitivity in classifying viral and bacterial pneumonia in comparison to radiologist 1 (5-year experience) and radiologist 2 (7-year experience). • The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist.
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Affiliation(s)
- Fang Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China
| | - Xiaoming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China
| | - Ru Wen
- Medical College, Guizhou University, Guiyang, Guizhou Province, 550000, China
| | - Hu Luo
- No 1. Intensive Care Unit, Huoshenshan Hospital, Wuhan, China
- Department of Respiratory and Critical Care Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Dong Liu
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Shuai Qi
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Yang Jing
- Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China
| | - Peng Wang
- Medical Big Data and Artificial Intelligence Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Gang Deng
- Department of Radiology, Maternal and Child Health Hospital of Hubei Province, Guanggu District, Wuhan, China
| | - Cong Huang
- Department of Radiology, The 926 Hospital of PLA, Kaiyuan, China
| | - Tingting Du
- Department of Radiology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Limei Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China
| | - Hongqin Liang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China.
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China.
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China.
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Hardy-Werbin M, Maiques JM, Busto M, Cirera I, Aguirre A, Garcia-Gisbert N, Zuccarino F, Carbullanca S, Del Carpio LA, Ramal D, Gayete Á, Martínez-Roldan J, Marquez-Colome A, Bellosillo B, Gibert J. MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis. Sci Rep 2023; 13:18761. [PMID: 37907750 PMCID: PMC10618492 DOI: 10.1038/s41598-023-46126-8] [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: 02/10/2023] [Accepted: 10/27/2023] [Indexed: 11/02/2023] Open
Abstract
The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure and Non-COVID Pneumonia and healthy (Control) patients. This retrospective single-center study includes CXR and blood test obtained between January 2017 and May 2020. Multi modal prediction models were generated using opensource DL algorithms. Performance of the MultiCOVID algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar-Bowker test. A total of 8578 samples from 6123 patients (mean age 66 ± 18 years of standard deviation, 3523 men) were evaluated across datasets. For the entire test set, the overall accuracy of MultiCOVID was 84%, with a mean AUC of 0.92 (0.89-0.94). For 300 random test images, overall accuracy of MultiCOVID was significantly higher (69.6%) compared with individual radiologists (range, 43.7-58.7%) and the consensus of all five radiologists (59.3%, P < .001). Overall, we have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-COVID pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.
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Affiliation(s)
- Max Hardy-Werbin
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Emergency Department, Hospital del Mar, Barcelona, Spain
| | | | - Marcos Busto
- Radiology Department, Hospital del Mar, Barcelona, Spain
| | - Isabel Cirera
- Emergency Department, Hospital del Mar, Barcelona, Spain
| | - Alfons Aguirre
- Emergency Department, Hospital del Mar, Barcelona, Spain
| | - Nieves Garcia-Gisbert
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | | | | | | | - Didac Ramal
- Radiology Department, Hospital del Mar, Barcelona, Spain
| | - Ángel Gayete
- Radiology Department, Hospital del Mar, Barcelona, Spain
| | - Jordi Martínez-Roldan
- Innovation and Digital Transformation Department, Hospital del Mar, Barcelona, Spain
| | | | - Beatriz Bellosillo
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Pathology Department, Hospital del Mar, Barcelona, Spain
| | - Joan Gibert
- Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
- Pathology Department, Hospital del Mar, Barcelona, Spain.
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Antony M, Kakileti ST, Shah R, Sahoo S, Bhattacharyya C, Manjunath G. Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19. Sci Rep 2023; 13:18102. [PMID: 37872204 PMCID: PMC10593822 DOI: 10.1038/s41598-023-44653-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 10/11/2023] [Indexed: 10/25/2023] Open
Abstract
Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones. However, the challenges associated with such systems have not been studied to the best of our knowledge. In this paper, we show that the predictions of AI-driven models on CXR images transmitted through Smart Phones via applications, such as WhatsApp, suffer both in terms of Predictability and Explainability, two key aspects of any automated Medical Diagnosis system. We find that several existing Deep learning based models exhibit prediction instability-disagreement between the prediction outcome of the original image and the transmitted image. Concomitantly we find that the explainability of the models deteriorate substantially, prediction on the transmitted CXR is often driven by features present outside the lung region, clearly a manifestation of Spurious Correlations. Our study reveals that there is significant compression of high-resolution CXR images, sometimes as high as 95%, and this could be the reason behind these two problems. Apart from demonstrating these problems, our main contribution is to show that Multi-Task learning (MTL) can serve as an effective bulwark against the aforementioned problems. We show that MTL models exhibit substantially more robustness, 40% over existing baselines. Explainability of such models, when measured by a saliency score dependent on out-of-lung features, also show a 35% improvement. The study is conducted on WaCXR dataset, a curated dataset of 6562 image pairs corresponding to original uncompressed and WhatsApp compressed CXR images. Keeping in mind that there are no previous datasets to study such problems, we open-source this data along with all implementations.
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Affiliation(s)
| | | | - Rachit Shah
- Indian Institute of Science, Bangalore, India
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11
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Miyazaki A, Ikejima K, Nishio M, Yabuta M, Matsuo H, Onoue K, Matsunaga T, Nishioka E, Kono A, Yamada D, Oba K, Ishikura R, Murakami T. Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system. Sci Rep 2023; 13:17533. [PMID: 37845348 PMCID: PMC10579343 DOI: 10.1038/s41598-023-44818-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 10/12/2023] [Indexed: 10/18/2023] Open
Abstract
To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following three categories: normal (NORMAL), non-COVID-19 pneumonia (PNEUMONIA), and COVID-19 pneumonia (COVID). We used two public datasets and private dataset collected from eight hospitals for the development and external validation of our DL model (26,393 CXRs). Eight radiologists performed two reading sessions: one session was performed with reference to CXRs only, and the other was performed with reference to both CXRs and the results of the DL model. The evaluation metrics for the reading session were accuracy, sensitivity, specificity, and area under the curve (AUC). The accuracy of our DL model was 0.733, and that of the eight radiologists without DL was 0.696 ± 0.031. There was a significant difference in AUC between the radiologists with and without DL for COVID versus NORMAL or PNEUMONIA (p = 0.0038). Our DL model alone showed better diagnostic performance than that of most radiologists. In addition, our model significantly improved the diagnostic performance of radiologists for COVID versus NORMAL or PNEUMONIA.
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Affiliation(s)
- Aki Miyazaki
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan
| | - Kengo Ikejima
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Mizuho Nishio
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
| | - Minoru Yabuta
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Hidetoshi Matsuo
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan
| | - Koji Onoue
- Department of Radiology, Kobe City Medical Center General Hospital, 2-1-1 Minatojimaminamimachi, Chuo-Ku, Kobe, 650-0047, Japan
- Department of Diagnostic Imaging and Interventional Radiology, Kyoto Katsura Hospital, 17 Yamada-Hirao, Nishikyo-Ku, Kyoto, 615-8256, Japan
| | - Takaaki Matsunaga
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan
| | - Eiko Nishioka
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan
| | - Atsushi Kono
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan
| | - Daisuke Yamada
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Ken Oba
- Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan
| | - Reiichi Ishikura
- Department of Radiology, Kobe City Medical Center General Hospital, 2-1-1 Minatojimaminamimachi, Chuo-Ku, Kobe, 650-0047, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan
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12
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Ullah Z, Usman M, Gwak J. MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 216:119475. [PMID: 36619348 PMCID: PMC9810379 DOI: 10.1016/j.eswa.2022.119475] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/28/2022] [Accepted: 12/22/2022] [Indexed: 06/12/2023]
Abstract
Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea
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13
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Khattab R, Abdelmaksoud IR, Abdelrazek S. Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey. NEW GENERATION COMPUTING 2023; 41:343-400. [PMID: 37229176 PMCID: PMC10071474 DOI: 10.1007/s00354-023-00213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.
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Affiliation(s)
- Rana Khattab
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Islam R. Abdelmaksoud
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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14
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S SRV, Sivapuram AK, Ravi V, Senthil G, Gorthi RK. VISAL-A novel learning strategy to address class imbalance. Neural Netw 2023; 161:178-184. [PMID: 36758464 DOI: 10.1016/j.neunet.2023.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 11/22/2022] [Accepted: 01/16/2023] [Indexed: 01/22/2023]
Abstract
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the imbalanced data classification task. VISAL's objective is to create more room for the generalization of minority class samples by bringing in both the angular and euclidean margins into the cross-entropy learning strategy. When evaluated on the imbalanced versions of CIFAR, Tiny ImageNet, COVIDx and IMDB reviews datasets, our proposed method outperforms the state of the art works by a significant margin.
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Affiliation(s)
| | | | - Vaishnavi Ravi
- Indian Institute of Technology, Tirupati, 517619, India.
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15
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Farhan AMQ, Yang S. Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-27. [PMID: 37362647 PMCID: PMC10030349 DOI: 10.1007/s11042-023-15047-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/30/2022] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
The chest X-ray images provide vital information about the congestion cost-effectively. We propose a novel Hybrid Deep Learning Algorithm (HDLA) framework for automatic lung disease classification from chest X-ray images. The model consists of steps including pre-processing of chest X-ray images, automatic feature extraction, and detection. In a pre-processing step, our goal is to improve the quality of raw chest X-ray images using the combination of optimal filtering without data loss. The robust Convolutional Neural Network (CNN) is proposed using the pre-trained model for automatic lung feature extraction. We employed the 2D CNN model for the optimum feature extraction in minimum time and space requirements. The proposed 2D CNN model ensures robust feature learning with highly efficient 1D feature estimation from the input pre-processed image. As the extracted 1D features have suffered from significant scale variations, we optimized them using min-max scaling. We classify the CNN features using the different machine learning classifiers such as AdaBoost, Support Vector Machine (SVM), Random Forest (RM), Backpropagation Neural Network (BNN), and Deep Neural Network (DNN). The experimental results claim that the proposed model improves the overall accuracy by 3.1% and reduces the computational complexity by 16.91% compared to state-of-the-art methods.
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Affiliation(s)
- Abobaker Mohammed Qasem Farhan
- School of information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shangming Yang
- School of information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
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16
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Nakashima M, Uchiyama Y, Minami H, Kasai S. Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs. J Med Radiat Sci 2023; 70:13-20. [PMID: 36334033 PMCID: PMC9877603 DOI: 10.1002/jmrs.631] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
INTRODUCTION Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images. METHODS In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance. RESULTS The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90). CONCLUSIONS We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.
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Affiliation(s)
- Maoko Nakashima
- Graduate School of Health SciencesKumamoto UniversityKumamotoJapan
| | - Yoshikazu Uchiyama
- Department of Medical Image Sciences, Faculty of Life SciencesKumamoto UniversityKumamotoJapan
| | | | - Satoshi Kasai
- Department of Radiological TechnologyNiigata University of Health and WelfareNiigataJapan
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17
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Moussaid A, Zrira N, Benmiloud I, Farahat Z, Karmoun Y, Benzidia Y, Mouline S, El Abdi B, Bourkadi JE, Ngote N. On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories. Healthcare (Basel) 2023; 11:healthcare11050662. [PMID: 36900667 PMCID: PMC10000749 DOI: 10.3390/healthcare11050662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/11/2023] [Accepted: 02/12/2023] [Indexed: 03/12/2023] Open
Abstract
The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at 99.81% and CT at 99.88%. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time.
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Affiliation(s)
- Abdelghani Moussaid
- MECAtronique Team, CPS2E Laboratory, National Superior School of Mines Rabat, Rabat 53000, Morocco
- ISITS-Maintenance Biomédicale-/Rabat, Abulcasis International University of Health Sciences, Rabat 10000, Morocco
- Correspondence:
| | - Nabila Zrira
- ADOS Team, LISTD Laboratory, National Superior School of Mines Rabat, Rabat 53000, Morocco
| | - Ibtissam Benmiloud
- MECAtronique Team, CPS2E Laboratory, National Superior School of Mines Rabat, Rabat 53000, Morocco
| | - Zineb Farahat
- SSDT Team, LISTD Laboratory, National Superior School of Mines Rabat, Rabat 53000, Morocco
- Medical Simulation Center/Rabat of the Cheikh Zaid Foundation, Rabat 10000, Morocco
| | - Youssef Karmoun
- ISITS-Maintenance Biomédicale-/Rabat, Abulcasis International University of Health Sciences, Rabat 10000, Morocco
| | - Yasmine Benzidia
- ISITS-Maintenance Biomédicale-/Rabat, Abulcasis International University of Health Sciences, Rabat 10000, Morocco
| | - Soumaya Mouline
- Cheikh Zaïd International University Hospital, B.P. 6533, Rabat 10000, Morocco
| | - Bahia El Abdi
- ISITS-Maintenance Biomédicale-/Rabat, Abulcasis International University of Health Sciences, Rabat 10000, Morocco
| | - Jamal Eddine Bourkadi
- Faculty of Medicine and Pharmacy, Mohammed V University, B.P. 6203, Rabat 10000, Morocco
| | - Nabil Ngote
- MECAtronique Team, CPS2E Laboratory, National Superior School of Mines Rabat, Rabat 53000, Morocco
- ISITS-Maintenance Biomédicale-/Rabat, Abulcasis International University of Health Sciences, Rabat 10000, Morocco
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Vinod DN, Prabaharan SRS. COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:2667-2682. [PMID: 36685135 PMCID: PMC9843670 DOI: 10.1007/s11831-023-09882-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 01/05/2023] [Indexed: 05/29/2023]
Abstract
The absolute previously infected novel coronavirus (COVID-19) was found in Wuhan, China, in December 2019. The COVID-19 epidemic has spread to more than 220 nations and territories globally and has altogether influenced each part of our day-to-day lives. As of 9th March 2022, a total aggregate of 44,78,82,185 (60,07,317) contaminated (dead) COVID-19 cases were accounted for all over the world. The quantities of contaminated cases passing despite everything increment essentially and do not indicate a controlled circumstance. The scope of this paper is to address this issue by presenting a comprehensive and comparative analysis of the existing Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) based approaches used in significance in reacting to the COVID-19 epidemic and diagnosing the severe impacts. The paper provides, firstly, an overview of COVID-19 infection and highlights of this article; Secondly, an overview of exploring various executive innovations by utilizing different resources to stop the spread of COVID-19; Thirdly, a comparison of existing predicting methods of COVID-19 in the literature, with focus on ML, DL and AI-driven techniques with performance metrics; and finally, a discussion on the results of the work as well as future scope.
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Affiliation(s)
- Dasari Naga Vinod
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062 India
| | - S. R. S. Prabaharan
- Sathyabama Centre for Advanced Studies, Sathyabama Institute of Science and Technology, Rajiv Gandhi Salai, Chennai, Tamil Nadu 600119 India
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19
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Kaya Y, Gürsoy E. A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection. Soft comput 2023; 27:5521-5535. [PMID: 36618761 PMCID: PMC9812349 DOI: 10.1007/s00500-022-07798-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/24/2022] [Indexed: 01/05/2023]
Abstract
COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model's performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19.
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Affiliation(s)
- Yasin Kaya
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Ercan Gürsoy
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
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20
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Garcea F, Serra A, Lamberti F, Morra L. Data augmentation for medical imaging: A systematic literature review. Comput Biol Med 2023; 152:106391. [PMID: 36549032 DOI: 10.1016/j.compbiomed.2022.106391] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/22/2022] [Accepted: 11/29/2022] [Indexed: 12/13/2022]
Abstract
Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. However, collecting large datasets for medical imaging is still a challenge due to privacy concerns and labeling costs. Data augmentation makes it possible to greatly expand the amount and variety of data available for training without actually collecting new samples. Data augmentation techniques range from simple yet surprisingly effective transformations such as cropping, padding, and flipping, to complex generative models. Depending on the nature of the input and the visual task, different data augmentation strategies are likely to perform differently. For this reason, it is conceivable that medical imaging requires specific augmentation strategies that generate plausible data samples and enable effective regularization of deep neural networks. Data augmentation can also be used to augment specific classes that are underrepresented in the training set, e.g., to generate artificial lesions. The goal of this systematic literature review is to investigate which data augmentation strategies are used in the medical domain and how they affect the performance of clinical tasks such as classification, segmentation, and lesion detection. To this end, a comprehensive analysis of more than 300 articles published in recent years (2018-2022) was conducted. The results highlight the effectiveness of data augmentation across organs, modalities, tasks, and dataset sizes, and suggest potential avenues for future research.
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Affiliation(s)
- Fabio Garcea
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Alessio Serra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Fabrizio Lamberti
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy.
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21
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A few-shot approach for COVID-19 screening in standard and portable chest X-ray images. Sci Rep 2022; 12:21511. [PMID: 36513713 PMCID: PMC9745688 DOI: 10.1038/s41598-022-25754-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/05/2022] [Indexed: 12/15/2022] Open
Abstract
Reliable and effective diagnostic systems are of vital importance for COVID-19, specifically for triage and screening procedures. In this work, a fully automatic diagnostic system based on chest X-ray images (CXR) has been proposed. It relies on the few-shot paradigm, which allows to work with small databases. Furthermore, three components have been added to improve the diagnosis performance: (1) a region proposal network which makes the system focus on the lungs; (2) a novel cost function which adds expert knowledge by giving specific penalties to each misdiagnosis; and (3) an ensembling procedure integrating multiple image comparisons to produce more reliable diagnoses. Moreover, the COVID-SC dataset has been introduced, comprising almost 1100 AnteroPosterior CXR images, namely 439 negative and 653 positive according to the RT-PCR test. Expert radiologists divided the negative images into three categories (normal lungs, COVID-related diseases, and other diseases) and the positive images into four severity levels. This entails the most complete COVID-19 dataset in terms of patient diversity. The proposed system has been compared with state-of-the-art methods in the COVIDGR-1.0 public database, achieving the highest accuracy (81.13% ± 2.76%) and the most robust results. An ablation study proved that each system component contributes to improve the overall performance. The procedure has also been validated on the COVID-SC dataset under different scenarios, with accuracies ranging from 70.81 to 87.40%. In conclusion, our proposal provides a good accuracy appropriate for the early detection of COVID-19.
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22
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Comprehensive Survey of Machine Learning Systems for COVID-19 Detection. J Imaging 2022; 8:jimaging8100267. [PMID: 36286361 PMCID: PMC9604704 DOI: 10.3390/jimaging8100267] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 01/14/2023] Open
Abstract
The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.
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Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
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Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
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Jangam E, Annavarapu CSR, Barreto AAD. A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14367-14401. [PMID: 36157353 PMCID: PMC9490695 DOI: 10.1007/s11042-022-13710-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 05/05/2022] [Accepted: 08/24/2022] [Indexed: 06/16/2023]
Abstract
To accurately diagnose multiple lung diseases from chest X-rays, the critical aspect is to identify lung diseases with high sensitivity and specificity. This study proposed a novel multi-class classification framework that minimises either false positives or false negatives that is useful in computer aided diagnosis or computer aided detection respectively. To minimise false positives or false negatives, we generated respective stacked ensemble from pre-trained models and fully connected layers using selection metric and systematic method. The diversity of base classifiers was based on diverse set of false positives or false negatives generated. The proposed multi-class framework was evaluated on two chest X-ray datasets, and the performance was compared with the existing models and base classifiers. Moreover, we used LIME (Local Interpretable Model-agnostic Explanations) to locate the regions focused by the multi-class classification framework.
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Affiliation(s)
- Ebenezer Jangam
- Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh India
- Department of Computer Science Engineering, Indian Institute of Technology(ISM), Dhanbad, Jharkhand India
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25
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Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2728866. [PMID: 36039344 PMCID: PMC9420061 DOI: 10.1155/2022/2728866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/13/2022] [Accepted: 07/05/2022] [Indexed: 11/17/2022]
Abstract
Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use. The Chest X-ray (CXR) and CT images were effectively utilized in screening purposes that could provide relevant data on localized regions affected by the infection. A step towards automated screening and diagnosis using CXR and CT could be of considerable importance in these turbulent times. The main objective is to probe a simple threshold-based segmentation approach to identify possible infection regions in CXR images and investigate intensity-based, wavelet transform (WT)-based, and Laws based texture features with statistical measures. Further feature selection strategy using Random Forest (RF) then selected features used to create Machine Learning (ML) representation with Support Vector Machine (SVM) and a Random Forest (RF) to make different COVID-19 from viral pneumonia (VP). The results obtained clearly indicate that the intensity and WT-based features vary in the two pathologies that are better differentiated with the combined features trained using SVM and RF classifiers. Classifier performance measures like an Area Under the Curve (AUC) of 0.97 and by and large classification accuracy of 0.9 using the RF model clearly indicate that the methodology implemented is useful in characterizing COVID-19 and Viral Pneumonia.
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26
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Fang L, Wang X. COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. Biocybern Biomed Eng 2022; 42:977-994. [PMID: 35945982 PMCID: PMC9353669 DOI: 10.1016/j.bbe.2022.07.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/10/2022] [Accepted: 07/31/2022] [Indexed: 12/23/2022]
Abstract
Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients.
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Affiliation(s)
- Lingling Fang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China
| | - Xin Wang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China
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27
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Kaushik B, Koundal D, Goel N, Zaguia A, Belay A, Turabieh H. Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7124199. [PMID: 35800691 PMCID: PMC9253872 DOI: 10.1155/2022/7124199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/24/2022] [Accepted: 04/30/2022] [Indexed: 12/04/2022]
Abstract
Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.
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Affiliation(s)
- Bhavana Kaushik
- School of Computer Science, University of Petroleum & Energy Studies, Bidholi, Dehradun, India
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Bidholi, Dehradun, India
| | - Neelam Goel
- University Institute of Engineering and Technology, Panjab University, Chandigarh, India
| | - Atef Zaguia
- Department of Information Technology, College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Assaye Belay
- Department of Statistics, Mizan-Tepi University, Tepi, Ethiopia
| | - Hamza Turabieh
- Department of Information Technology, College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Rajaraman S, Guo P, Xue Z, Antani SK. A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays. Diagnostics (Basel) 2022; 12:diagnostics12061442. [PMID: 35741252 PMCID: PMC9221627 DOI: 10.3390/diagnostics12061442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/05/2022] [Accepted: 06/08/2022] [Indexed: 12/02/2022] Open
Abstract
Pneumonia is an acute respiratory infectious disease caused by bacteria, fungi, or viruses. Fluid-filled lungs due to the disease result in painful breathing difficulties and reduced oxygen intake. Effective diagnosis is critical for appropriate and timely treatment and improving survival. Chest X-rays (CXRs) are routinely used to screen for the infection. Computer-aided detection methods using conventional deep learning (DL) models for identifying pneumonia-consistent manifestations in CXRs have demonstrated superiority over traditional machine learning approaches. However, their performance is still inadequate to aid in clinical decision-making. This study improves upon the state of the art as follows. Specifically, we train a DL classifier on large collections of CXR images to develop a CXR modality-specific model. Next, we use this model as the classifier backbone in the RetinaNet object detection network. We also initialize this backbone using random weights and ImageNet-pretrained weights. Finally, we construct an ensemble of the best-performing models resulting in improved detection of pneumonia-consistent findings. Experimental results demonstrate that an ensemble of the top-3 performing RetinaNet models outperformed individual models in terms of the mean average precision (mAP) metric (0.3272, 95% CI: (0.3006,0.3538)) toward this task, which is markedly higher than the state of the art (mAP: 0.2547). This performance improvement is attributed to the key modifications in initializing the weights of classifier backbones and constructing model ensembles to reduce prediction variance compared to individual constituent models.
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29
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Rodrigues LF, Backes AR, Travençolo BAN, de Oliveira GMB. Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification. J Digit Imaging 2022; 35:623-637. [PMID: 35199257 PMCID: PMC9156643 DOI: 10.1007/s10278-022-00600-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 01/21/2022] [Accepted: 01/28/2022] [Indexed: 12/15/2022] Open
Abstract
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cells in the bone marrow. Computer vision techniques provide automated analysis that can help specialists diagnose this disease. Microscopy image analysis is the most economical method for the initial screening of patients with ALL, but this task is subjective and time-consuming. In this study, we propose a hybrid model using a genetic algorithm (GA) and a residual convolutional neural network (CNN), ResNet-50V2, to predict ALL using microscopy images available in ALL-IDB dataset. However, accurate prediction requires suitable hyperparameters setup, and tuning these values manually still poses challenges. Hence, this paper uses GA to find the best hyperparameters that lead to the highest accuracy rate in the models. Also, we compare the performance of GA hyperparameter optimization with Random Search and Bayesian optimization methods. The results show that GA optimization improves the accuracy of the classifier, obtaining 98.46% in terms of accuracy. Additionally, our approach sheds new perspectives on identifying leukemia based on computer vision strategies, which could be an alternative for applications in a real-world scenario.
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Affiliation(s)
| | - André Ricardo Backes
- Faculty of Computing (FACOM), Federal University of Uberlândia (UFU), Uberlândia, MG Brazil
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30
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COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104861] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The coronavirus pandemic started in Wuhan, China in December 2019, and put millions of people in a difficult situation. This fatal virus spread to over 227 countries and the number of infected patients increased to over 400 million cases, causing over 6 million deaths worldwide. Due to the serious consequence of this virus, it is necessary to develop a detection method that can respond quickly to prevent the spreading of COVID-19. Using chest X-ray images to detect COVID-19 is one of the promising techniques; however, with a large number of COVID-19 infected cases every day, the number of radiologists available to diagnose the chest X-ray images is not sufficient. We must have a computer aid system that helps doctors instantly and automatically determine COVID-19 cases. Recently, with the emergence of deep learning methods applied for medical and biomedical uses, using convolutional neural net and transformer applications for chest X-ray images can be a supplement for COVID-19 testing. In this paper, we attempt to classify three types of chest X-ray, which are normal, pneumonia, and COVID-19 using deep learning methods on a customized dataset. We also carry out an experiment on the COVID-19 severity assessment task using a tailored dataset. Five deep learning models were obtained to conduct our experiments: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EfficientNet-DOLG neural networks. The results indicated that chest X-ray and deep learning could be reliable methods for supporting doctors in COVID-19 identification and severity assessment tasks.
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Kong L, Cheng J. Classification and Detection of COVID-19 X-Ray Images based on DenseNet and VGG16 Feature Fusion. Biomed Signal Process Control 2022; 77:103772. [PMID: 35573817 PMCID: PMC9080057 DOI: 10.1016/j.bspc.2022.103772] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 04/22/2022] [Accepted: 04/27/2022] [Indexed: 12/12/2022]
Abstract
Since December 2019, the novel coronavirus disease (COVID-19) caused by the syndrome coronavirus 2 (SARS-CoV-2) strain has spread widely around the world and has become a serious global public health problem. For this high-speed infectious disease, the application of X-ray to chest diagnosis plays a key role. In this study, we propose a chest X-ray image classification method based on feature fusion of a dense convolutional network (DenseNet) and a visual geometry group network (VGG16). This paper adds an attention mechanism (global attention machine block and category attention block) to the model to extract deep features. A residual network (ResNet) is used to segment effective image information to quickly achieve accurate classification. The average accuracy of our model in detecting binary classification can reach 98.0%. The average accuracy for three category classification can reach 97.3%. The experimental results show that the proposed model has good results in this work. Therefore, the use of deep learning and feature fusion technology in the classification of chest X-ray images can become an auxiliary tool for clinicians and radiologists.
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Affiliation(s)
- Lingzhi Kong
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
| | - Jinyong Cheng
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
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32
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Santosh KC, Ghosh S, GhoshRoy D. Deep Learning for Covid-19 Screening Using Chest X-Rays in 2020: A Systematic Review. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422520103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Artificial Intelligence (AI) has promoted countless contributions in the field of healthcare and medical imaging. In this paper, we thoroughly analyze peer-reviewed research findings/articles on AI-guided tools for Covid-19 analysis/screening using chest X-ray images in the year 2020. We discuss on how far deep learning algorithms help in decision-making. We identify/address data collections, methodical contributions, promising methods, and challenges. However, a fair comparison is not trivial as dataset sizes vary over time, throughout the year 2020. Even though their unprecedented efforts in building AI-guided tools to detect, localize, and segment Covid-19 cases are limited to education and training, we elaborate on their strengths and possible weaknesses when we consider the need of cross-population train/test models. In total, with search keywords: (Covid-19 OR Coronavirus) AND chest x-ray AND deep learning AND artificial intelligence AND medical imaging in both PubMed Central Repository and Web of Science, we systematically reviewed 58 research articles and performed meta-analysis.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab – Computer Science, University of South Dakota, Vermillion, SD 57069, USA
| | - Supriti Ghosh
- 2AI: Applied Artificial Intelligence Research Lab – Computer Science, University of South Dakota, Vermillion, SD 57069, USA
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Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083895] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted to confirm positive COVID-19 RT-PCR tests. Since the very beginning of the pandemic, researchers in the artificial intelligence area have proposed a large number of automatic diagnosing models, hoping to assist radiologists and improve the diagnosing accuracy. However, after two years of development, there are still few models that can actually be applied in real-world scenarios. Numerous problems have emerged in the research of the automated diagnosis of COVID-19. In this paper, we present a systematic review of these diagnosing models. A total of 179 proposed models are involved. First, we compare the medical image modalities (CT or X-ray) for COVID-19 diagnosis from both the clinical perspective and the artificial intelligence perspective. Then, we classify existing methods into two types—image-level diagnosis (i.e., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models). For both types of methods, we define universal model pipelines and analyze the techniques that have been applied in each step of the pipeline in detail. In addition, we also review some commonly adopted public COVID-19 datasets. More importantly, we present an in-depth discussion of the existing automated diagnosis models and note a total of three significant problems: biased model performance evaluation; inappropriate implementation details; and a low reproducibility, reliability and explainability. For each point, we give corresponding recommendations on how we can avoid making the same mistakes and let AI perform better in the next pandemic.
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Hu Z, Yang Z, Lafata KJ, Yin FF, Wang C. A radiomics-boosted deep-learning model for COVID-19 and Non-COVID-19 pneumonia classification using chest X-ray images. Med Phys 2022; 49:3213-3222. [PMID: 35263458 PMCID: PMC9088469 DOI: 10.1002/mp.15582] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 02/08/2022] [Accepted: 02/12/2022] [Indexed: 01/08/2023] Open
Abstract
PURPOSE To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray images. METHODS As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest X-ray image; thus, each feature is rendered as a 2D map in the same dimension as the X-ray image. Based on each of the three investigated deep neural network architectures, including VGG-16, VGG-19, and DenseNet-121, a pilot model was trained using X-ray images only. Subsequently, 2 radiomic feature maps (RFMs) were selected based on cross-correlation analysis in reference to the pilot model saliency map results. The radiomics-boosted model was then trained based on the same deep neural network architecture using X-ray images plus the selected RFMs as input. The proposed radiomics-boosted design was developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. For each model, 50 runs were trained with random assignments of training/validation cases following the 7:1 ratio in the training-validation set. Sensitivity, specificity, accuracy, and ROC curves together with Area-Under-the-Curve (AUC) from all three deep neural network architectures were evaluated. RESULTS After radiomics-boosted implementation, all three investigated deep neural network architectures demonstrated improved sensitivity, specificity, accuracy, and ROC AUC results in COVID-19 and healthy individual classifications. VGG-16 showed the largest improvement in COVID-19 classification ROC (AUC from 0.963 to 0.993), and DenseNet-121 showed the largest improvement in healthy individual classification ROC (AUC from 0.962 to 0.989). The reduced variations suggested improved robustness of the model to data partition. For the challenging Non-COVID-19 pneumonia classification task, radiomics-boosted implementation of VGG-16 (AUC from 0.918 to 0.969) and VGG-19 (AUC from 0.964 to 0.970) improved ROC results, while DenseNet-121 showed a slight yet insignificant ROC performance reduction (AUC from 0.963 to 0.949). The achieved highest accuracy of COVID-19/Non-COVID-19 pneumonia/healthy individual classifications were 0.973 (VGG-19)/0.936 (VGG-19)/ 0.933 (VGG-16), respectively. CONCLUSIONS The inclusion of radiomic analysis in deep learning model design improved the performance and robustness of COVID-19/Non-COVID-19 pneumonia/healthy individual classification, which holds great potential for clinical applications in the COVID-19 pandemic. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zongsheng Hu
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316, China
| | - Zhenyu Yang
- Department of Radiation Oncology, Duke University, Durham, NC, 27710
| | - Kyle J Lafata
- Department of Radiation Oncology, Duke University, Durham, NC, 27710.,Department of Radiology, Duke University, Durham, NC, 27710.,Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27710
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316, China.,Department of Radiation Oncology, Duke University, Durham, NC, 27710
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, NC, 27710
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35
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Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5998042. [PMID: 35251572 PMCID: PMC8890832 DOI: 10.1155/2022/5998042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/13/2021] [Accepted: 01/07/2022] [Indexed: 12/20/2022]
Abstract
Pulmonary medical image analysis using image processing and deep learning approaches has made remarkable achievements in the diagnosis, prognosis, and severity check of lung diseases. The epidemic of COVID-19 brought out by the novel coronavirus has triggered a critical need for artificial intelligence assistance in diagnosing and controlling the disease to reduce its effects on people and global economies. This study aimed at identifying the various COVID-19 medical imaging analysis models proposed by different researchers and featured their merits and demerits. It gives a detailed discussion on the existing COVID-19 detection methodologies (diagnosis, prognosis, and severity/risk detection) and the challenges encountered for the same. It also highlights the various preprocessing and post-processing methods involved to enhance the detection mechanism. This work also tries to bring out the different unexplored research areas that are available for medical image analysis and how the vast research done for COVID-19 can advance the field. Despite deep learning methods presenting high levels of efficiency, some limitations have been briefly described in the study. Hence, this review can help understand the utilization and pros and cons of deep learning in analyzing medical images.
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Malhotra A, Mittal S, Majumdar P, Chhabra S, Thakral K, Vatsa M, Singh R, Chaudhury S, Pudrod A, Agrawal A. Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images. PATTERN RECOGNITION 2022; 122:108243. [PMID: 34456368 PMCID: PMC8379001 DOI: 10.1016/j.patcog.2021.108243] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 07/06/2021] [Accepted: 08/08/2021] [Indexed: 05/07/2023]
Abstract
With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster. Chest X-Ray is one of the modalities that is gaining acceptance as a screening modality. Towards this direction, the paper has two primary contributions. Firstly, we present the COVID-19 Multi-Task Network (COMiT-Net) which is an automated end-to-end network for COVID-19 screening. The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable. Secondly, with the help of medical professionals, we manually annotate the lung regions and semantic segmentation of COVID19 symptoms in CXRs taken from the ChestXray-14, CheXpert, and a consolidated COVID-19 dataset. These annotations will be released to the research community. Experiments performed with more than 2500 frontal CXR images show that at 90% specificity, the proposed COMiT-Net yields 96.80% sensitivity.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Ashwin Pudrod
- Ashwini Hospital and Ramakant Heart Care Centre, 431602, India
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Khan IU, Aslam N, Anwar T, Alsaif HS, Chrouf SMB, Alzahrani NA, Alamoudi FA, Kamaleldin MMA, Awary KB. Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray. SENSORS 2022; 22:s22020669. [PMID: 35062629 PMCID: PMC8779361 DOI: 10.3390/s22020669] [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] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 12/18/2022]
Abstract
The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.
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Affiliation(s)
- Irfan Ullah Khan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Nida Aslam
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
- Correspondence:
| | - Talha Anwar
- School of Computing, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan;
| | - Hind S. Alsaif
- Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (H.S.A.); (K.B.A.)
| | - Sara Mhd. Bachar Chrouf
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Norah A. Alzahrani
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh 12391, Saudi Arabia
| | - Fatimah Ahmed Alamoudi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Mariam Moataz Aly Kamaleldin
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (I.U.K.); (S.M.B.C.); (N.A.A.); (F.A.A.); (M.M.A.K.)
| | - Khaled Bassam Awary
- Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (H.S.A.); (K.B.A.)
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38
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Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images. Healthcare (Basel) 2022; 10:healthcare10010166. [PMID: 35052328 PMCID: PMC8776223 DOI: 10.3390/healthcare10010166] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 12/11/2022] Open
Abstract
Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.
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Hossain MB, Iqbal SMHS, Islam MM, Akhtar MN, Sarker IH. Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images. INFORMATICS IN MEDICINE UNLOCKED 2022; 30:100916. [PMID: 35342787 PMCID: PMC8933872 DOI: 10.1016/j.imu.2022.100916] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/07/2022] [Accepted: 03/09/2022] [Indexed: 12/13/2022] Open
Abstract
COVID-19 cases are putting pressure on healthcare systems all around the world. Due to the lack of available testing kits, it is impractical for screening every patient with a respiratory ailment using traditional methods (RT-PCR). In addition, the tests have a high turn-around time and low sensitivity. Detecting suspected COVID-19 infections from the chest X-ray might help isolate high-risk people before the RT-PCR test. Most healthcare systems already have X-ray equipment, and because most current X-ray systems have already been computerized, there is no need to transfer the samples. The use of a chest X-ray to prioritize the selection of patients for subsequent RT-PCR testing is the motivation of this work. Transfer learning (TL) with fine-tuning on deep convolutional neural network-based ResNet50 model has been proposed in this work to classify COVID-19 patients from the COVID-19 Radiography Database. Ten distinct pre-trained weights, trained on varieties of large-scale datasets using various approaches such as supervised learning, self-supervised learning, and others, have been utilized in this work. Our proposed i N a t 2021 _ M i n i _ S w A V _ 1 k model, pre-trained on the iNat2021 Mini dataset using the SwAV algorithm, outperforms the other ResNet50 TL models. For COVID instances in the two-class (Covid and Normal) classification, our work achieved 99.17% validation accuracy, 99.95% train accuracy, 99.31% precision, 99.03% sensitivity, and 99.17% F1-score. Some domain-adapted ( I m a g e N e t _ C h e s t X - r a y 14 ) and in-domain (ChexPert, ChestX-ray14) models looked promising in medical image classification by scoring significantly higher than other models.
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Affiliation(s)
- Md Belal Hossain
- Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh
| | - S M Hasan Sazzad Iqbal
- Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh
| | - Md Monirul Islam
- Department of Textile Engineering, Uttara University, Dhaka 1230, Bangladesh
| | - Md Nasim Akhtar
- Department of Computer Science and Engineering, Dhaka University of Engineering Technology, Gazipur, 1707, Bangladesh
| | - Iqbal H Sarker
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh
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40
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An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108094] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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41
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Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112210528] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.
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42
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Zhao W, Jiang W, Qiu X. Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images. Diagnostics (Basel) 2021; 11:1887. [PMID: 34679585 PMCID: PMC8535063 DOI: 10.3390/diagnostics11101887] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/30/2021] [Accepted: 10/10/2021] [Indexed: 12/24/2022] Open
Abstract
As the COVID-19 pandemic continues to ravage the world, the use of chest X-ray (CXR) images as a complementary screening strategy to reverse transcription-polymerase chain reaction (RT-PCR) testing continues to grow owing to its routine clinical application to respiratory diseases. We performed extensive convolutional neural network (CNN) fine-tuning experiments and identified that models pretrained on larger out-of-domain datasets show an improved performance. This suggests that a priori knowledge of models from out-of-field training should also apply to X-ray images. With appropriate hyperparameters selection, we found that higher resolution images carry more clinical information, and the use of mixup in training improved the performance of the model. The experimental showed that our proposed transfer learning present state-of-the-art results. Furthermore, we evaluated the performance of our model with a small amount of downstream training data and found that the model still performed well in COVID-19 identification. We also explored the mechanism of model detection using a gradient-weighted class activation mapping (Grad-CAM) method for CXR imaging to interpret the detection of radiology images. The results helped us understand how the model detects COVID-19, which can be used to discover new visual features and assist radiologists in screening.
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Affiliation(s)
- Wentao Zhao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; (W.Z.); (X.Q.)
- School of Intelligent Transportation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China
| | - Wei Jiang
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; (W.Z.); (X.Q.)
| | - Xinguo Qiu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China; (W.Z.); (X.Q.)
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43
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A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11198867] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.
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44
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Javidi M, Abbaasi S, Naybandi Atashi S, Jampour M. COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet. Sci Rep 2021; 11:18478. [PMID: 34531477 PMCID: PMC8446042 DOI: 10.1038/s41598-021-97901-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/01/2021] [Indexed: 12/24/2022] Open
Abstract
With the presence of novel coronavirus disease at the end of 2019, several approaches were proposed to help physicians detect the disease, such as using deep learning to recognize lung involvement based on the pattern of pneumonia. These approaches rely on analyzing the CT images and exploring the COVID-19 pathologies in the lung. Most of the successful methods are based on the deep learning technique, which is state-of-the-art. Nevertheless, the big drawback of the deep approaches is their need for many samples, which is not always possible. This work proposes a combined deep architecture that benefits both employed architectures of DenseNet and CapsNet. To more generalize the deep model, we propose a regularization term with much fewer parameters. The network convergence significantly improved, especially when the number of training data is small. We also propose a novel Cost-sensitive loss function for imbalanced data that makes our model feasible for the condition with a limited number of positive data. Our novelties make our approach more intelligent and potent in real-world situations with imbalanced data, popular in hospitals. We analyzed our approach on two publicly available datasets, HUST and COVID-CT, with different protocols. In the first protocol of HUST, we followed the original paper setup and outperformed it. With the second protocol of HUST, we show our approach superiority concerning imbalanced data. Finally, with three different validations of the COVID-CT, we provide evaluations in the presence of a low number of data along with a comparison with state-of-the-art.
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Affiliation(s)
| | | | - Sara Naybandi Atashi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
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45
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Okolo GI, Katsigiannis S, Althobaiti T, Ramzan N. On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays. SENSORS (BASEL, SWITZERLAND) 2021; 21:5702. [PMID: 34502591 PMCID: PMC8434119 DOI: 10.3390/s21175702] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/17/2021] [Accepted: 08/20/2021] [Indexed: 01/08/2023]
Abstract
The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.
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Affiliation(s)
- Gabriel Iluebe Okolo
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK;
| | | | - Turke Althobaiti
- Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia;
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK;
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46
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Zhang Z, Chen B, Sun J, Luo Y. A bagging dynamic deep learning network for diagnosing COVID-19. Sci Rep 2021; 11:16280. [PMID: 34381079 PMCID: PMC8358001 DOI: 10.1038/s41598-021-95537-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 01/19/2023] Open
Abstract
COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.
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Affiliation(s)
- Zhijun Zhang
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, 510335, China.
- School of Automation Science and Engineering, East China Jiaotong University, Nanchang, 330052, China.
- Shaanxi Provincial Key Laboratory of Industrial Automation, School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China.
- School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China.
| | - Bozhao Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Jiansheng Sun
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Yamei Luo
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
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47
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Sadre R, Sundaram B, Majumdar S, Ushizima D. Validating deep learning inference during chest X-ray classification for COVID-19 screening. Sci Rep 2021; 11:16075. [PMID: 34373530 PMCID: PMC8352989 DOI: 10.1038/s41598-021-95561-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/26/2021] [Indexed: 12/24/2022] Open
Abstract
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.
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Affiliation(s)
- Robbie Sadre
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Baskaran Sundaram
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, 19107, USA
| | - Sharmila Majumdar
- Institute for Computational Health Sciences, University of California, San Francisco, CA, 94117, USA
| | - Daniela Ushizima
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
- Institute for Computational Health Sciences, University of California, San Francisco, CA, 94117, USA.
- Berkeley Institute for Data Science, University of California, Berkeley, CA, 94720, USA.
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48
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Momeny M, Neshat AA, Hussain MA, Kia S, Marhamati M, Jahanbakhshi A, Hamarneh G. Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images. Comput Biol Med 2021; 136:104704. [PMID: 34352454 PMCID: PMC8760424 DOI: 10.1016/j.compbiomed.2021.104704] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/14/2021] [Accepted: 07/23/2021] [Indexed: 12/21/2022]
Abstract
Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy.
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Affiliation(s)
- Mohammad Momeny
- Department of Computer Engineering, Yazd University, Yazd, Iran.
| | - Ali Asghar Neshat
- Department of Environmental Engineering, Esfarayen Faculty of Medical Science, Esfarayen, Iran.
| | | | - Solmaz Kia
- Department of Engineering Science, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran
| | - Mahmoud Marhamati
- Department of Medical-Surgical Nursing, Esfarayen Faculty of Medical Science, Esfarayen, Iran
| | - Ahmad Jahanbakhshi
- Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Ghassan Hamarneh
- School of Computing Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
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Nishio M, Fujimoto K, Matsuo H, Muramatsu C, Sakamoto R, Fujita H. Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network. Front Artif Intell 2021; 4:694815. [PMID: 34337394 PMCID: PMC8322116 DOI: 10.3389/frai.2021.694815] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Abstract
Purpose: The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. The pretrained model was constructed from an artificial dataset generated using a generative adversarial network (GAN). Materials and Methods: Three public datasets containing images of lung nodules/lung cancers were used: LUNA16 dataset, Decathlon lung dataset, and NSCLC radiogenomics. The LUNA16 dataset was used to generate an artificial dataset for lung cancer segmentation with the help of the GAN and 3D graph cut. Pretrained models were then constructed from the artificial dataset. Subsequently, the main segmentation model was constructed from the pretrained models and the Decathlon lung dataset. Finally, the NSCLC radiogenomics dataset was used to evaluate the main segmentation model. The Dice similarity coefficient (DSC) was used as a metric to evaluate the segmentation performance. Results: The mean DSC for the NSCLC radiogenomics dataset improved overall when using the pretrained models. At maximum, the mean DSC was 0.09 higher with the pretrained model than that without it. Conclusion: The proposed method comprising an artificial dataset and a pretrained model can improve lung cancer segmentation as confirmed in terms of the DSC metric. Moreover, the construction of the artificial dataset for the segmentation using the GAN and 3D graph cut was found to be feasible.
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Affiliation(s)
- Mizuho Nishio
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Radiology, Kobe University Hospital, Kobe, Japan
| | - Koji Fujimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Real World Data Research and Development, Kyoto University Graduate School of Medicine, Hikone, Japan
| | | | | | - Ryo Sakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
| | - Hiroshi Fujita
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu, Japan
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Jangam E, Annavarapu CSR. A stacked ensemble for the detection of COVID-19 with high recall and accuracy. Comput Biol Med 2021; 135:104608. [PMID: 34247135 PMCID: PMC8241584 DOI: 10.1016/j.compbiomed.2021.104608] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/04/2021] [Accepted: 06/22/2021] [Indexed: 12/24/2022]
Abstract
The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having low sensitivity (or recall). Hence, developing diagnostic techniques with high recall and automatic feature extraction using the available data are crucial for controlling the spread of COVID-19. This paper proposes a novel stacked ensemble capable of detecting COVID-19 from a patient's chest CT scans with high recall and accuracy. A systematic approach for designing a stacked ensemble from pre-trained computer vision models using transfer learning (TL) is presented. A novel diversity measure that results in the stacked ensemble with high recall and accuracy is proposed. The stacked ensemble proposed in this paper considers four pre-trained computer vision models: the visual geometry group (VGG)-19, residual network (ResNet)-101, densely connected convolutional network (DenseNet)-169 and wide residual network (WideResNet)-50-2. The proposed model was trained and evaluated with three different chest CT scans. As recall is more important than precision, the trade-offs between recall and precision were explored in relevance to COVID-19. The optimal recommended threshold values were found for each dataset.
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Affiliation(s)
- Ebenezer Jangam
- Department of Information Technology, VRSiddhartha Engineering College, Vijayawada, Andhra Pradesh, India; Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India
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