1
|
Hao B, Hu Y, Adams WG, Assoumou SA, Hsu HE, Bhadelia N, Paschalidis IC. A GPT-based EHR modeling system for unsupervised novel disease detection. J Biomed Inform 2024; 157:104706. [PMID: 39121932 DOI: 10.1016/j.jbi.2024.104706] [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: 04/26/2024] [Revised: 07/17/2024] [Accepted: 08/06/2024] [Indexed: 08/12/2024]
Abstract
OBJECTIVE To develop an Artificial Intelligence (AI)-based anomaly detection model as a complement of an "astute physician" in detecting novel disease cases in a hospital and preventing emerging outbreaks. METHODS Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was designed and further trained using Empirical Risk Minimization (ERM), which can model a hospitalized patient's Electronic Health Records (EHR) and detect atypical patients. Methods and performance metrics, similar to the ones behind the recent Large Language Models (LLMs), were leveraged to capture the dynamic evolution of the patient's clinical variables and compute an Out-Of-Distribution (OOD) anomaly score. RESULTS In a completely unsupervised setting, hospitalizations for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection could have been predicted by our GPT model at the beginning of the COVID-19 pandemic, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 92.2 %, using 31 extracted clinical variables and a 3-day detection window. Our GPT achieves individual patient-level anomaly detection and mortality prediction AUC of 78.3 % and 94.7 %, outperforming traditional linear models by 6.6 % and 9 %, respectively. Different types of clinical trajectories of a SARS-CoV-2 infection are captured by our model to make interpretable detections, while a trend of over-pessimistic outcome prediction yields a more effective detection pathway. Furthermore, our comprehensive GPT model can potentially assist clinicians with forecasting patient clinical variables and developing personalized treatment plans. CONCLUSION This study demonstrates that an emerging outbreak can be accurately detected within a hospital, by using a GPT to model patient EHR time sequences and labeling them as anomalous when actual outcomes are not supported by the model. Such a GPT is also a comprehensive model with the functionality of generating future patient clinical variables, which can potentially assist clinicians in developing personalized treatment plans.
Collapse
Affiliation(s)
- Boran Hao
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Yang Hu
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - William G Adams
- Department of Pediatrics, Boston Medical Center, Boston, MA, USA; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Sabrina A Assoumou
- Department of Medicine, Boston Medical Center, Boston, MA, USA; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Heather E Hsu
- Department of Pediatrics, Boston Medical Center, Boston, MA, USA; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
| | - Nahid Bhadelia
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Center for Emerging Infectious Diseases Policy and Research, Boston University, Boston, MA, USA
| | - Ioannis Ch Paschalidis
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA; Department of Biomedical Engineering, Division of Systems Engineering, Faculty of Computing & Data Sciences, and Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, MA, USA.
| |
Collapse
|
2
|
Sang H, Lee H, Lee M, Park J, Kim S, Woo HG, Rahmati M, Koyanagi A, Smith L, Lee S, Hwang YC, Park TS, Lim H, Yon DK, Rhee SY. Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts. Sci Rep 2024; 14:14966. [PMID: 38942775 PMCID: PMC11213851 DOI: 10.1038/s41598-024-63798-y] [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: 12/27/2023] [Accepted: 06/03/2024] [Indexed: 06/30/2024] Open
Abstract
This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic complication in these patients. We used data from two cohorts, namely the discovery (one hospital; n = 12,809) and validation (two hospitals; n = 2019) cohorts, recruited between 2008 and 2022. The outcome of interest was the presence or absence of CVD at 3 years. We selected various ML-based models with hyperparameter tuning in the discovery cohort and performed area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. CVD was observed in 1238 (10.2%) patients in the discovery cohort. The random forest (RF) model exhibited the best overall performance among the models, with an AUROC of 0.830 (95% confidence interval [CI] 0.818-0.842) in the discovery dataset and 0.722 (95% CI 0.660-0.783) in the validation dataset. Creatinine and glycated hemoglobin levels were the most influential factors in the RF model. This study introduces a pioneering ML-based model for predicting CVD in Korean patients with T2DM, outperforming existing prediction tools and providing a groundbreaking approach for early personalized preventive medicine.
Collapse
Affiliation(s)
- Hyunji Sang
- Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea
- Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Hojae Lee
- Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
- Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
| | - Myeongcheol Lee
- Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
- Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
| | - Jaeyu Park
- Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
- Department of Regulatory Science, Kyung Hee University, Seoul, South Korea
| | - Sunyoung Kim
- Department of Family Medicine, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Ho Geol Woo
- Department of Neurology, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea
| | - Masoud Rahmati
- Research Centre on Health Services and Quality of Life, Aix Marseille University, Marseille, France
- Department of Physical Education and Sport Sciences, Faculty of Literature and Human Sciences, Lorestan University, Khoramabad, Iran
- Department of Physical Education and Sport Sciences, Faculty of Literature and Humanities, Vali-E-Asr University of Rafsanjan, Rafsanjan, Iran
| | - Ai Koyanagi
- Research and Development Unit, Parc Sanitari Sant Joan de Deu, Barcelona, Spain
| | - Lee Smith
- Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK
| | - Sihoon Lee
- Department of Internal Medicine, Gachon University College of Medicine, Incheon, South Korea
| | - You-Cheol Hwang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong and Kyung Hee University School of Medicine, Seoul, South Korea
| | - Tae Sun Park
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University and Jeonbuk National University Hospital, Jeonju, South Korea
| | - Hyunjung Lim
- Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin, South Korea
| | - Dong Keon Yon
- Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
- Department of Pediatrics, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea.
- Department of Regulatory Science, Kyung Hee University, Seoul, South Korea.
| | - Sang Youl Rhee
- Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea.
- Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea.
- Department of Regulatory Science, Kyung Hee University, Seoul, South Korea.
| |
Collapse
|
3
|
Alalhareth M, Hong SC. Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems. SENSORS (BASEL, SWITZERLAND) 2024; 24:3519. [PMID: 38894310 PMCID: PMC11175330 DOI: 10.3390/s24113519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/20/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024]
Abstract
This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model's superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.
Collapse
Affiliation(s)
- Mousa Alalhareth
- Department of Information Systems, College of Computer Science and Information System, Najran University, Najran 61441, Saudi Arabia
- Department of Computer and Information Sciences, Towson University, Towson, MD 21204, USA
| | - Sung-Chul Hong
- Department of Computer and Information Sciences, Towson University, Towson, MD 21204, USA
| |
Collapse
|
4
|
Veluchamy S, Sudharson S, Annamalai R, Bassfar Z, Aljaedi A, Jamal SS. Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01077-y. [PMID: 38499705 DOI: 10.1007/s10278-024-01077-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/19/2023] [Accepted: 01/14/2024] [Indexed: 03/20/2024]
Abstract
The incidence of COVID-19, a virus that is responsible for infections in the upper respiratory tract and lungs, witnessed a daily rise in fatalities throughout the pandemic. The timely identification of COVID-19 can contribute to the formulation of strategies to control the disease and the selection of an appropriate treatment pathway. Given the necessity for broader COVID-19 diagnosis, researchers have developed more advanced, rapid, and efficient detection methods. By conducting an initial comparative analysis of various widely used convolutional neural network (CNN) models, we determine an appropriate CNN model. Subsequently, we enhance the chosen CNN model using the feature fusion strategy from multi-modal imaging datasets. Moreover, the Jaya optimization technique is employed to determine the optimal weighting for merging these dual features into a single feature vector. An SVM classifier is employed to categorize samples as either COVID-19 positive or negative. For the purpose of experimentation, a standard dataset consisting of 10,000 samples is used, divided equally between COVID-19 positive and negative classes. The experimental outcomes demonstrate that the proposed fine-tuned system, coupled with optimization techniques for multi-modal data, exhibits superior performance, achieving accuracy rates of 98.7% as compared to the existing state-of-the-art network models.
Collapse
Affiliation(s)
- S Veluchamy
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, 601103, India
| | - S Sudharson
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India.
| | - R Annamalai
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, 601103, India
| | - Zaid Bassfar
- Department of Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
| | - Amer Aljaedi
- College of Computing and Information Technology, University of Tabuk, Tabuk, 71491, Saudi Arabia
| | - Sajjad Shaukat Jamal
- Department of Mathematics, College of Science, King Khalid University, Abha, 61413, Saudi Arabia
| |
Collapse
|
5
|
Abdullah M, Abrha FB, Kedir B, Tamirat Tagesse T. A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays. Heliyon 2024; 10:e26938. [PMID: 38468922 PMCID: PMC10926074 DOI: 10.1016/j.heliyon.2024.e26938] [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: 03/29/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/13/2024] Open
Abstract
Coronavirus disease (COVID-2019) is emerging in Wuhan, China in 2019. It has spread throughout the world since the year 2020. Millions of people were affected and caused death to them till now. To avoid the spreading of COVID-2019, various precautions and restrictions have been taken by all nations. At the same time, infected persons are needed to identify and isolate, and medical treatment should be provided to them. Due to a deficient number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, a Chest X-ray image is becoming an effective technique for diagnosing COVID-19. In this work, the Hybrid Deep Learning CNN model is proposed for the diagnosis COVID-19 using chest X-rays. The proposed model consists of a heading model and a base model. The base model utilizes two pre-trained deep learning structures such as VGG16 and VGG19. The feature dimensions from these pre-trained models are reduced by incorporating different pooling layers, such as max and average. In the heading part, dense layers of size three with different activation functions are also added. A dropout layer is supplemented to avoid overfitting. The experimental analyses are conducted to identify the efficacy of the proposed hybrid deep learning with existing transfer learning architectures such as VGG16, VGG19, EfficientNetB0 and ResNet50 using a COVID-19 radiology database. Various classification techniques, such as K-Nearest Neighbor (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), and Neural Network, were also used for the performance comparison of the proposed model. The hybrid deep learning model with average pooling layers, along with SVM-linear and neural networks, both achieved an accuracy of 92%.These proposed models can be employed to assist radiologists and physicians in avoiding misdiagnosis rates and to validate the positive COVID-19 infected cases.
Collapse
Affiliation(s)
- Mohan Abdullah
- Department of Electrical and Computer Engineering, Wachemo University, Ethiopia
| | - Ftsum berhe Abrha
- Department of Electrical and Computer Engineering, Wachemo University, Ethiopia
| | - Beshir Kedir
- Department of Electrical and Computer Engineering, Wachemo University, Ethiopia
| | | |
Collapse
|
6
|
Fili V, Savelonas M. Self-attention-driven retrieval of chest CT images for COVID-19 assessment. Biomed Phys Eng Express 2024; 10:025013. [PMID: 38224614 DOI: 10.1088/2057-1976/ad1e76] [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: 09/02/2023] [Accepted: 01/15/2024] [Indexed: 01/17/2024]
Abstract
Numerous methods have been developed for computer-aided diagnosis (CAD) of coronavirus disease-19 (COVID-19), based on chest computed tomography (CT) images. The majority of these methods are based on deep neural networks and often act as "black boxes" that cannot easily gain the trust of medical community, whereas their result is uniformly influenced by all image regions. This work introduces a novel, self-attention-driven method for content-based image retrieval (CBIR) of chest CT images. The proposed method analyzes a query CT image and returns a classification result, as well as a list of classified images, ranked according to similarity with the query. Each CT image is accompanied by a heatmap, which is derived by gradient-weighted class activation mapping (Grad-CAM) and represents the contribution of lung tissue and lesions to COVID-19 pathology. Beyond visualization, Grad-CAM weights are employed in a self-attention mechanism, in order to strengthen the influence of the most COVID-19-related image regions on the retrieval result. Experiments on two publicly available datasets demonstrate that the binary classification accuracy obtained by means of DenseNet-201 is 81.3% and 96.4%, for COVID-CT and SARS-CoV-2 datasets, respectively, with a false negative rate which is less than 3% in both datasets. In addition, the Grad-CAM-guided CBIR framework slightly outperforms the plain CBIR in most cases, with respect to nearest neighbour (NN) and first four (FF). The proposed method could serve as a computational tool for a more transparent decision-making process that could be trusted by the medical community. In addition, the employed self-attention mechanism increases the obtained retrieval performance.
Collapse
Affiliation(s)
- Victoria Fili
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Lamia, 35131, Greece
| | - Michalis Savelonas
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Lamia, 35131, Greece
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Vinothini R, Niranjana G, Yakub F. A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images. J Digit Imaging 2023; 36:2480-2493. [PMID: 37491543 PMCID: PMC10584759 DOI: 10.1007/s10278-023-00852-7] [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: 02/22/2023] [Revised: 05/13/2023] [Accepted: 05/15/2023] [Indexed: 07/27/2023] Open
Abstract
The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.
Collapse
Affiliation(s)
- R Vinothini
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India.
| | - G Niranjana
- SRM Institute of Science and Technology, Kattankulathur, India
| | - Fitri Yakub
- Electronic System Engineering Department, Malaysia-Japan International Institute of Technology, Kuala Lumpur, Malaysia
| |
Collapse
|
9
|
Tiwari RS, Dandabani L, Das TK, Khan SB, Basheer S, Alqahtani MS. Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants. Diagnostics (Basel) 2023; 13:3419. [PMID: 37998555 PMCID: PMC10670372 DOI: 10.3390/diagnostics13223419] [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: 10/08/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023] Open
Abstract
The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network-Capsule Network (CapsNet)-and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model's remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely.
Collapse
Affiliation(s)
- Ravi Shekhar Tiwari
- Department of Computer Science Engineering, Mahindra University, Hyderabad 500043, India
| | - Lakshmi Dandabani
- School of Computing Science and Engineering, VIT Bhopal University, Bhopal 466114, India;
| | - Tapan Kumar Das
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Mohammed S. Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia;
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester LE1 7RH, UK
| |
Collapse
|
10
|
Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
Collapse
Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
| |
Collapse
|
11
|
Xie P, Zhao X, He X. Improve the performance of CT-based pneumonia classification via source data reweighting. Sci Rep 2023; 13:9401. [PMID: 37296239 PMCID: PMC10251339 DOI: 10.1038/s41598-023-35938-3] [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: 10/09/2022] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Pneumonia is a life-threatening disease. Computer tomography (CT) imaging is broadly used for diagnosing pneumonia. To assist radiologists in accurately and efficiently detecting pneumonia from CT scans, many deep learning methods have been developed. These methods require large amounts of annotated CT scans, which are difficult to obtain due to privacy concerns and high annotation costs. To address this problem, we develop a three-level optimization based method which leverages CT data from a source domain to mitigate the lack of labeled CT scans in a target domain. Our method automatically identifies and downweights low-quality source CT data examples which are noisy or have large domain discrepancy with target data, by minimizing the validation loss of a target model trained on reweighted source data. On a target dataset with 2218 CT scans and a source dataset with 349 CT images, our method achieves an F1 score of 91.8% in detecting pneumonia and an F1 score of 92.4% in detecting other types of pneumonia, which are significantly better than those achieved by state-of-the-art baseline methods.
Collapse
Affiliation(s)
- Pengtao Xie
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, USA.
| | - Xingchen Zhao
- Department of Electrical and Computer Engineering, Northeastern University, Boston, USA
| | - Xuehai He
- Department of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, USA
| |
Collapse
|
12
|
Dubey AK, Chabert GL, Carriero A, Pasche A, Danna PSC, Agarwal S, Mohanty L, Sharma N, Yadav S, Jain A, Kumar A, Kalra MK, Sobel DW, Laird JR, Singh IM, Singh N, Tsoulfas G, Fouda MM, Alizad A, Kitas GD, Khanna NN, Viskovic K, Kukuljan M, Al-Maini M, El-Baz A, Saba L, Suri JS. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework. Diagnostics (Basel) 2023; 13:diagnostics13111954. [PMID: 37296806 DOI: 10.3390/diagnostics13111954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/22/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND AND MOTIVATION Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.
Collapse
Affiliation(s)
- Arun Kumar Dubey
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Alessio Pasche
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Pietro S C Danna
- Department of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
| | - Lopamudra Mohanty
- ABES Engineering College, Ghaziabad 201009, India
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
| | - Sarita Yadav
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Achin Jain
- Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India
| | - Ashish Kumar
- Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - David W Sobel
- Men's Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Azra Alizad
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - Melita Kukuljan
- Department of Interventional and Diagnostic Radiology, Clinical Hospital Center Rijeka, 51000 Rijeka, Croatia
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology & Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Ayman El-Baz
- Biomedical Engineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| |
Collapse
|
13
|
Ajagbe SA, Adigun MO. Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-35. [PMID: 37362693 PMCID: PMC10226029 DOI: 10.1007/s11042-023-15805-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 03/06/2023] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps in the timely detection of any infectious disease (IDs) and is essential to the management of diseases and the prediction of future occurrences. Many scientists and scholars have implemented DL techniques for the detection and prediction of pandemics, IDs and other healthcare-related purposes, these outcomes are with various limitations and research gaps. For the purpose of achieving an accurate, efficient and less complicated DL-based system for the detection and prediction of pandemics, therefore, this study carried out a systematic literature review (SLR) on the detection and prediction of pandemics using DL techniques. The survey is anchored by four objectives and a state-of-the-art review of forty-five papers out of seven hundred and ninety papers retrieved from different scholarly databases was carried out in this study to analyze and evaluate the trend of DL techniques application areas in the detection and prediction of pandemics. This study used various tables and graphs to analyze the extracted related articles from various online scholarly repositories and the analysis showed that DL techniques have a good tool in pandemic detection and prediction. Scopus and Web of Science repositories are given attention in this current because they contain suitable scientific findings in the subject area. Finally, the state-of-the-art review presents forty-four (44) studies of various DL technique performances. The challenges identified from the literature include the low performance of the model due to computational complexities, improper labeling and the absence of a high-quality dataset among others. This survey suggests possible solutions such as the development of improved DL-based techniques or the reduction of the output layer of DL-based architecture for the detection and prediction of pandemic-prone diseases as future considerations.
Collapse
Affiliation(s)
- Sunday Adeola Ajagbe
- Department of Computer & Industrial Production Engineering, First Technical University Ibadan, Ibadan, 200255 Nigeria
- Department of Computer Science, University of Zululand, Kwadlangezwa, 3886 South Africa
| | - Matthew O. Adigun
- Department of Computer Science, University of Zululand, Kwadlangezwa, 3886 South Africa
| |
Collapse
|
14
|
Hanis TM, Ruhaiyem NIR, Arifin WN, Haron J, Wan Abdul Rahman WF, Abdullah R, Musa KI. Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning. Diagnostics (Basel) 2023; 13:diagnostics13101780. [PMID: 37238264 DOI: 10.3390/diagnostics13101780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 05/28/2023] Open
Abstract
Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transfer learning and digital mammograms. The digital mammograms and their associated information were collected from the department of radiology and pathology at Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and tested in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks whose ranking was based on PR-AUC values, precision, and F1 scores. The final ensemble model, which consisted of Resnet101, Resnet152, and ResNet50V2, had a mean precision value, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated balanced performance across mammographic density. In conclusion, this study demonstrates the good performance of ensemble transfer learning and digital mammograms in breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus reducing their workloads and further improving the medical workflow in the screening and diagnosis of breast cancer.
Collapse
Affiliation(s)
- Tengku Muhammad Hanis
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | | | - Wan Nor Arifin
- Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Juhara Haron
- Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
- Breast Cancer Awareness and Research Unit, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Wan Faiziah Wan Abdul Rahman
- Breast Cancer Awareness and Research Unit, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
- Department of Pathology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Rosni Abdullah
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| |
Collapse
|
15
|
Chen X, Bai Y, Wang P, Luo J. Data augmentation based semi-supervised method to improve COVID-19 CT classification. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:6838-6852. [PMID: 37161130 DOI: 10.3934/mbe.2023294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL.
Collapse
Affiliation(s)
- Xiangtao Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Yuting Bai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| | - Peng Wang
- College of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China
| |
Collapse
|
16
|
Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans. INTELLIGENT SYSTEMS WITH APPLICATIONS 2023; 17:200160. [PMCID: PMC9708108 DOI: 10.1016/j.iswa.2022.200160] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/21/2022] [Accepted: 11/27/2022] [Indexed: 10/27/2023]
Abstract
COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes.
Collapse
|
17
|
Esmi N, Golshan Y, Asadi S, Shahbahrami A, Gaydadjiev G. A fuzzy fine-tuned model for COVID-19 diagnosis. Comput Biol Med 2023; 153:106483. [PMID: 36621192 PMCID: PMC9811914 DOI: 10.1016/j.compbiomed.2022.106483] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/16/2022] [Accepted: 12/25/2022] [Indexed: 01/06/2023]
Abstract
The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.
Collapse
Affiliation(s)
- Nima Esmi
- Faculty of Science and Engineering, University of Groningen, Netherlands.
| | - Yasaman Golshan
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Sara Asadi
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Asadollah Shahbahrami
- Faculty of Science and Engineering, University of Groningen, Netherlands; Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Georgi Gaydadjiev
- Faculty of Science and Engineering, University of Groningen, Netherlands.
| |
Collapse
|
18
|
Alaiad AI, Mugdadi EA, Hmeidi II, Obeidat N, Abualigah L. Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning. J Med Biol Eng 2023; 43:135-146. [PMID: 37077696 PMCID: PMC10010231 DOI: 10.1007/s40846-023-00783-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 02/16/2023] [Indexed: 04/21/2023]
Abstract
Purpose Coronavirus 2019 (COVID-19) had major social, medical, and economic impacts globally. The study aims to develop a deep-learning model that can predict the severity of COVID-19 in patients based on CT images of their lungs. Methods COVID-19 causes lung infections, and qRT-PCR is an essential tool used to detect virus infection. However, qRT-PCR is inadequate for detecting the severity of the disease and the extent to which it affects the lung. In this paper, we aim to determine the severity level of COVID-19 by studying lung CT scans of people diagnosed with the virus. Results We used images from King Abdullah University Hospital in Jordan; we collected our dataset from 875 cases with 2205 CT images. A radiologist classified the images into four levels of severity: normal, mild, moderate, and severe. We used various deep-learning algorithms to predict the severity of lung diseases. The results show that the best deep-learning algorithm used is Resnet101, with an accuracy score of 99.5% and a data loss rate of 0.03%. Conclusion The proposed model assisted in diagnosing and treating COVID-19 patients and helped improve patient outcomes.
Collapse
Affiliation(s)
- Ahmad Imwafak Alaiad
- Computer Information System, Jordan University of Science and Technology, Irbid, Jordan
| | - Esraa Ahmad Mugdadi
- Computer Information System, Jordan University of Science and Technology, Irbid, Jordan
| | - Ismail Ibrahim Hmeidi
- Computer Information System, Jordan University of Science and Technology, Irbid, Jordan
| | - Naser Obeidat
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113 Jordan
- College of Engineering, Yuan Ze University, Taoyuan, Taiwan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
- Applied Science Research Center, Applied Science Private University, Amman, 11931 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
| |
Collapse
|
19
|
Kathamuthu ND, Subramaniam S, Le QH, Muthusamy S, Panchal H, Sundararajan SCM, Alrubaie AJ, Zahra MMA. A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. ADVANCES IN ENGINEERING SOFTWARE (BARKING, LONDON, ENGLAND : 1992) 2023; 175:103317. [PMID: 36311489 PMCID: PMC9595382 DOI: 10.1016/j.advengsoft.2022.103317] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/09/2022] [Accepted: 10/19/2022] [Indexed: 05/26/2023]
Abstract
The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.
Collapse
Affiliation(s)
- Nirmala Devi Kathamuthu
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Shanthi Subramaniam
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Quynh Hoang Le
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
| | - Suresh Muthusamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Hitesh Panchal
- Department of Mechanical Engineering, Government Engineering College, Patan, Gujarat, India
| | | | - Ali Jawad Alrubaie
- Department of Medical Instrumentation Techniques Engineering, Al- Mustaqbal University College, 51001, Hilla, Iraq
| | - Musaddak Maher Abdul Zahra
- Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
| |
Collapse
|
20
|
Zhang HT, Sun ZY, Zhou J, Gao S, Dong JH, Liu Y, Bai X, Ma JL, Li M, Li G, Cai JM, Sheng FG. Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling. Front Cell Infect Microbiol 2023; 13:1116285. [PMID: 36936770 PMCID: PMC10020619 DOI: 10.3389/fcimb.2023.1116285] [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: 12/05/2022] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Background There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. Methods A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. Results The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. Conclusions This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.
Collapse
Affiliation(s)
- Hong-Tao Zhang
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ze-Yu Sun
- Algorithm Center, Keya Medical Technology Co., Ltd, Shenzhen, China
| | - Juan Zhou
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shen Gao
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jing-Hui Dong
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yuan Liu
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xu Bai
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jin-Lin Ma
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ming Li
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Guang Li
- Algorithm Center, Keya Medical Technology Co., Ltd, Shenzhen, China
| | - Jian-Ming Cai
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Jian-Ming Cai, ; Fu-Geng Sheng,
| | - Fu-Geng Sheng
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Jian-Ming Cai, ; Fu-Geng Sheng,
| |
Collapse
|
21
|
Xu Y, Lam HK, Jia G, Jiang J, Liao J, Bao X. Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation. Comput Biol Med 2023; 152:106417. [PMID: 36543003 PMCID: PMC9750504 DOI: 10.1016/j.compbiomed.2022.106417] [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: 08/08/2022] [Revised: 11/22/2022] [Accepted: 12/04/2022] [Indexed: 12/23/2022]
Abstract
The COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. However, challenges still remain, including low data diversity in existing datasets, and unsatisfied detection resulting from insufficient accuracy and sensitivity of deep learning models. To enhance the data diversity, we design augmentation techniques of incremental levels and apply them to the largest open-access benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR) derived from contrastive learning is proposed in this study to enable CNNs to learn more parameter-efficient representations, thus improve the accuracy and sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that CNN performance can be improved stably through applying the designed augmentation and SR techniques. In particular, DenseNet121 with SR achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia. The achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These statistics suggest that our method has surpassed the existing state-of-the-art methods on the COVIDx CT-2A dataset. Source code is available at https://github.com/YujiaKCL/COVID-CT-Similarity-Regularization.
Collapse
Affiliation(s)
- Yujia Xu
- Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom.
| | - Hak-Keung Lam
- Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom.
| | - Guangyu Jia
- Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom.
| | - Jian Jiang
- Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom.
| | - Junkai Liao
- Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom.
| | - Xinqi Bao
- Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom.
| |
Collapse
|
22
|
Wang Y, Li X, Xu J, Zhou Q. A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study. Sci Rep 2022; 12:18262. [PMID: 36309538 PMCID: PMC9617603 DOI: 10.1038/s41598-022-23285-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/27/2022] [Indexed: 12/31/2022] Open
Abstract
Many resource-limited countries need an efficient and convenient method to assess disease progression in patients with coronavirus disease 2019 (COVID-19). This study developed and validated a complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19. We collected the clinical data and laboratory test results of 86 patients with moderate COVID-19. These data were categorized into two subgroups depending on the laboratory test time. Univariate logistic regression and covariance diagnosis were used to screen for independent factors, and multifactorial logistic regression was used for model building. Data from 38 patients at another hospital were collected for external verification of the model. Basophils (OR 6.372; 95% CI 3.284-12.363), mean corpuscular volume (OR 1.244; 95% CI 1.088-1.422), red blood cell distribution width (OR 2.585; 95% CI 1.261-5.297), and platelet distribution width (OR 1.559; 95% CI 1.154-2.108) could be combined to predict recovery of patients with moderate COVID-19. The ROC curve showed that the model has good discrimination. The calibration curve showed that the model was well-fitted. The DCA showed that the model is clinically useful. Small increases in the above parameters within the normal range suggest an improvement in patients with moderate COVID-19.
Collapse
Affiliation(s)
- Yiting Wang
- grid.430605.40000 0004 1758 4110Department of Laboratory Medicine, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021 China
| | - Xuewen Li
- grid.430605.40000 0004 1758 4110Department of Laboratory Medicine, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021 China
| | - Jiancheng Xu
- grid.430605.40000 0004 1758 4110Department of Laboratory Medicine, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021 China
| | - Qi Zhou
- grid.430605.40000 0004 1758 4110Department of Pediatrics, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021 China
| |
Collapse
|
23
|
Liu S, Cai T, Tang X, Zhang Y, Wang C. COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention. Comput Biol Med 2022; 149:106065. [PMID: 36081225 PMCID: PMC9433340 DOI: 10.1016/j.compbiomed.2022.106065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/07/2022] [Accepted: 08/27/2022] [Indexed: 12/11/2022]
Abstract
Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.
Collapse
|
24
|
Ghose P, Alavi M, Tabassum M, Ashraf Uddin M, Biswas M, Mahbub K, Gaur L, Mallik S, Zhao Z. Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Front Genet 2022; 13:980338. [PMID: 36212141 PMCID: PMC9533058 DOI: 10.3389/fgene.2022.980338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022] Open
Abstract
COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.
Collapse
Affiliation(s)
- Partho Ghose
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
| | - Muhaddid Alavi
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Mehnaz Tabassum
- Center for Health Informatics, Macquarie University, Sydney, NSW, Australia
| | - Md. Ashraf Uddin
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Milon Biswas
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
| | - Kawsher Mahbub
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
| | - Loveleen Gaur
- Amity International Business School, Amity University, Noida, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, United States
| | - Zhongming Zhao
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, United States
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- *Correspondence: Zhongming Zhao,
| |
Collapse
|
25
|
Consuegra-Ayala JP, Gutiérrez Y, Almeida-Cruz Y, Palomar M. Intelligent ensembling of auto-ML system outputs for solving classification problems. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
26
|
Al-Areqi F, Konyar MZ. Effectiveness evaluation of different feature extraction methods for classification of covid-19 from computed tomography images: A high accuracy classification study. Biomed Signal Process Control 2022; 76:103662. [PMID: 35350595 PMCID: PMC8947946 DOI: 10.1016/j.bspc.2022.103662] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/18/2022] [Accepted: 03/19/2022] [Indexed: 01/16/2023]
Abstract
Rapid diagnosis of the Covid-19 disease is the best way to prevent infection. In this paper, it is proposed to use machine learning methods to aid diagnoses quickly Covid-19 and focused on effect of several features on classification accuracy. In the proposed method 746 axial computed tomography (CT) images of the lung; 349 Covid-19 (positives) and 397 non-Covid-19 (negative) are used. Gray-level texture, shape and first order statistical features were extracted from the images. The feature vector for model training is constructed with one feature group or combination of more than one group. We then classified with Support Vector Machine, Random Forest, k-nearest neighbor and XGBoost classifier models. The hyperparameter of the models were controlled by the tuning test. Experimental results obtained with 10-fold cross-validation. The results of cross-validation verified with the additionally independent test. The best overall accuracy was 98.65% with first order statistics features classified with XGBoost. In the gray level features, the best individual results given by GLSZM as 81.25%, and the best combination result is with GLDM, GLRLM and GLSZM features as 85.52%. An important finding of this paper is that, for Covid-19 classification, the shape and first order statistics features are more valuable than gray level features. The proposed results compared with the literature studies under some Covid-19 dataset for accuracy, precision, sensitivity and F1-score metrics. Also, the literature studies which used the different Covid-19 dataset were compared with the proposed study. Our results have the significant superiority when compared with the literature studies.
Collapse
Affiliation(s)
- Farid Al-Areqi
- Department of Biomedical Engineering, Kocaeli University, 41001 Kocaeli, Turkey
| | - Mehmet Zeki Konyar
- Department of Software Engineering, Kocaeli University, 41001 Kocaeli, Turkey
| |
Collapse
|
27
|
Yaren Tekin B, Ozcan C, Pekince A, Yasa Y. An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs. Comput Biol Med 2022; 146:105547. [DOI: 10.1016/j.compbiomed.2022.105547] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/29/2022] [Accepted: 04/17/2022] [Indexed: 12/21/2022]
|
28
|
Baghdadi NA, Malki A, Abdelaliem SF, Magdy Balaha H, Badawy M, Elhosseini M. An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network. Comput Biol Med 2022; 144:105383. [PMID: 35290811 PMCID: PMC8906898 DOI: 10.1016/j.compbiomed.2022.105383] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 02/06/2023]
Abstract
Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets, the development of such AI tools can prove challenging. Therefore, an algorithm for performing automatic and accurate COVID-19 classification using Convolutional Neural Network (CNN), pre-trained model, and Sparrow search algorithm (SSA) on CT lung images was proposed. The pre-trained CNN models used are SeresNext50, SeresNext101, SeNet154, MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large. In addition, the SSA will be used to optimize the different CNN and transfer learning(TL) hyperparameters to find the best configuration for the pre-trained model used and enhance its performance. Two datasets are used in the experiments. There are two classes in the first dataset, while three in the second. The authors combined two publicly available COVID-19 datasets as the first dataset, namely the COVID-19 Lung CT Scans and COVID-19 CT Scan Dataset. In total, 14,486 images were included in this study. The authors analyzed the Large COVID-19 CT scan slice dataset in the second dataset, which utilized 17,104 images. Compared to other pre-trained models on both classes datasets, MobileNetV3Large pre-trained is the best model. As far as the three-classes dataset is concerned, a model trained on SeNet154 is the best available. Results show that, when compared to other CNN models like LeNet-5 CNN, COVID faster R–CNN, Light CNN, Fuzzy + CNN, Dynamic CNN, CNN and Optimized CNN, the proposed Framework achieves the best accuracy of 99.74% (two classes) and 98% (three classes).
Collapse
Affiliation(s)
- Nadiah A Baghdadi
- Princess Nourah bint Abdulrahman University, College of Nursing, Riyadh, 11671, Riyadh, P.O. BOX 84428, Saudi Arabia.
| | - Amer Malki
- Taibah University, College of Computer Science and Engineering, Yanbu, 46421, Saudi Arabia
| | - Sally F Abdelaliem
- Princess Nourah bint Abdulrahman University, College of Nursing, Riyadh, 11671, Riyadh, P.O. BOX 84428, Saudi Arabia
| | - Hossam Magdy Balaha
- Mansoura University, Faculty of Engineering, Computers and Control Systems Engineering Department, Mansoura, 46421, Egypt
| | - Mahmoud Badawy
- Mansoura University, Faculty of Engineering, Computers and Control Systems Engineering Department, Mansoura, 46421, Egypt.
| | - Mostafa Elhosseini
- Taibah University, College of Computer Science and Engineering, Yanbu, 46421, Saudi Arabia; Mansoura University, Faculty of Engineering, Computers and Control Systems Engineering Department, Mansoura, 46421, Egypt
| |
Collapse
|
29
|
Mahanty C, Kumar R, Patro SGK. Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models. NEW GENERATION COMPUTING 2022; 40:1125-1141. [PMID: 35730008 PMCID: PMC9202670 DOI: 10.1007/s00354-022-00176-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/24/2022] [Indexed: 05/12/2023]
Abstract
One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders.
Collapse
Affiliation(s)
| | - Raghvendra Kumar
- Department of Computer Science and Engineering, GIET University, Gunupur, India
| | - S. Gopal Krishna Patro
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh India
| |
Collapse
|