1
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Santosh Kumar Patra P, Tripathy B. Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system. Comput Biol Med 2024; 181:109031. [PMID: 39173484 DOI: 10.1016/j.compbiomed.2024.109031] [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: 03/25/2024] [Revised: 08/11/2024] [Accepted: 08/13/2024] [Indexed: 08/24/2024]
Abstract
The COVID-19 pandemic has necessitated the development of innovative and efficient methods for early detection and diagnosis. Integrating Internet of Things (IoT) devices and applications in healthcare has facilitated various functions. This work aims to employ practical artificial intelligence (AI) approaches to extract meaningful information from the vast amount of IoT data to perform disease prediction tasks. However, traditional AI methods need help in feature analysis due to the complexity and scale of IoT data. So, this work implements the optimal iterative COVID-19 classification network (OICC-Net) using machine learning optimization and deep learning approaches. Initially, the preprocessing operation normalizes the dataset with uniform values. Here, random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm was used to get the disease-specific patterns from SARS-CoV-2 (SC2), and other disease classes, where patterns of the SC2 virus are very similar to those of other virus classes. In addition, an iterative deep convolution learning (IDCL) feature selection method is used to distinguish features from the RFI-PS-BWO data. This iterative process enhances the performance of feature selection by providing improved representation and reducing the dimensionality of the input data. Then, a one-dimensional convolutional neural network (1D-CNN) was employed to classify and identify the extracted features from SC2 with no virus classes. The 1D-CNN model is trained using a large dataset of COVID-19 samples, enabling it to learn intricate patterns and make accurate predictions. It was tested and found that the proposed OICC-Net system is more accurate than current methods, with a score of 99.97 % for F1-score, 100 % for sensitivity, 100 % for specificity, 99.98 % for precision, and 99.99 % for recall.
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Affiliation(s)
- P Santosh Kumar Patra
- Research Scholar, Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, 769015, India.
| | - Biswajit Tripathy
- Professor, Master of Computer Applications, Einstein College of Computer Application and Management, Khurda, Odisha, 752060, India.
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2
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Han Q, Qian X, Xu H, Wu K, Meng L, Qiu Z, Weng T, Zhou B, Gao X. DM-CNN: Dynamic Multi-scale Convolutional Neural Network with uncertainty quantification for medical image classification. Comput Biol Med 2024; 168:107758. [PMID: 38042102 DOI: 10.1016/j.compbiomed.2023.107758] [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: 08/25/2023] [Revised: 10/30/2023] [Accepted: 11/21/2023] [Indexed: 12/04/2023]
Abstract
Convolutional neural network (CNN) has promoted the development of diagnosis technology of medical images. However, the performance of CNN is limited by insufficient feature information and inaccurate attention weight. Previous works have improved the accuracy and speed of CNN but ignored the uncertainty of the prediction, that is to say, uncertainty of CNN has not received enough attention. Therefore, it is still a great challenge for extracting effective features and uncertainty quantification of medical deep learning models In order to solve the above problems, this paper proposes a novel convolutional neural network model named DM-CNN, which mainly contains the four proposed sub-modules : dynamic multi-scale feature fusion module (DMFF), hierarchical dynamic uncertainty quantifies attention (HDUQ-Attention) and multi-scale fusion pooling method (MF Pooling) and multi-objective loss (MO loss). DMFF select different convolution kernels according to the feature maps at different levels, extract different-scale feature information, and make the feature information of each layer have stronger representation ability for information fusion HDUQ-Attention includes a tuning block that adjust the attention weight according to the different information of each layer, and a Monte-Carlo (MC) dropout structure for quantifying uncertainty MF Pooling is a pooling method designed for multi-scale models, which can speed up the calculation and prevent overfitting while retaining the main important information Because the number of parameters in the backbone part of DM-CNN is different from other modules, MO loss is proposed, which has a fast optimization speed and good classification effect DM-CNN conducts experiments on publicly available datasets in four areas of medicine (Dermatology, Histopathology, Respirology, Ophthalmology), achieving state-of-the-art classification performance on all datasets. DM-CNN can not only maintain excellent performance, but also solve the problem of quantification of uncertainty, which is a very important task for the medical field. The code is available: https://github.com/QIANXIN22/DM-CNN.
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Affiliation(s)
- Qi Han
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Xin Qian
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China.
| | - Hongxiang Xu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Kepeng Wu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Lun Meng
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Zicheng Qiu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Tengfei Weng
- School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, PR China
| | - Baoping Zhou
- School of Information Engineering, Tarim University, Alar City, 843300, PR China
| | - Xianqiang Gao
- School of Information Engineering, Tarim University, Alar City, 843300, PR China
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3
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Talukder MA, Layek MA, Kazi M, Uddin MA, Aryal S. Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture. Comput Biol Med 2024; 168:107789. [PMID: 38042105 DOI: 10.1016/j.compbiomed.2023.107789] [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: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/28/2023] [Indexed: 12/04/2023]
Abstract
The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.
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Affiliation(s)
- Md Alamin Talukder
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Md Abu Layek
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Mohsin Kazi
- Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box-2457, Riyadh 11451, Saudi Arabia.
| | - Md Ashraf Uddin
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia.
| | - Sunil Aryal
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia.
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4
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Pal M, Mahal A, Mohapatra RK, Obaidullah AJ, Sahoo RN, Pattnaik G, Pattanaik S, Mishra S, Aljeldah M, Alissa M, Najim MA, Alshengeti A, AlShehail BM, Garout M, Halwani MA, Alshehri AA, Rabaan AA. Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification. ACS OMEGA 2023; 8:31747-31757. [PMID: 37692219 PMCID: PMC10483519 DOI: 10.1021/acsomega.3c02784] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 08/09/2023] [Indexed: 09/12/2023]
Abstract
The world faces multiple public health emergencies simultaneously, such as COVID-19 and Monkeypox (mpox). mpox, from being a neglected disease, has emerged as a global threat that has spread to more than 100 nonendemic countries, even as COVID-19 has been spreading for more than 3 years now. The general mpox symptoms are similar to chickenpox and measles, thus leading to a possible misdiagnosis. This study aimed at facilitating a rapid and high-brevity mpox diagnosis. Reportedly, mpox circulates among particular groups, such as sexually promiscuous gay and bisexuals. Hence, selectively vaccinating, isolating, and treating them seems difficult due to the associated social stigma. Deep learning (DL) has great promise in image-based diagnosis and could help in error-free bulk diagnosis. The novelty proposed, the system adopted, and the methods and approaches are discussed in the article. The present work proposes the use of DL models for automated early mpox diagnosis. The performances of the proposed algorithms were evaluated using the data set available in public domain. The data set adopted for the study was meant for both training and testing, the details of which are elaborated. The performances of CNN, VGG19, ResNet 50, Inception v3, and Autoencoder algorithms were compared. It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) classification accuracy.
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Affiliation(s)
- Madhumita Pal
- Department
of Electrical Engineering, Government College
of Engineering, Keonjhar, Odisha 758 002, India
| | - Ahmed Mahal
- Department
of Medical Biochemical Analysis, College of Health Technology, Cihan University−Erbil, Erbil, Kurdistan Region, Iraq
| | - Ranjan K. Mohapatra
- Department
of Chemistry, Government College of Engineering, Keonjhar, Odisha 758 002, India
| | - Ahmad J. Obaidullah
- Department
of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Rudra Narayan Sahoo
- School
of Pharmaceutical Sciences, Siksha ‘O’
Anusandhan (Deemed to be University), Kalinga Nagar, Bhubaneswar, Odisha 751 003, India
| | - Gurudutta Pattnaik
- School of
Pharmacy and Life Sciences, Centurion University
of Technology and Management, Khordha , Odisha 752 050, India
| | - Sovan Pattanaik
- School
of Pharmaceutical Sciences, Siksha ‘O’
Anusandhan (Deemed to be University), Kalinga Nagar, Bhubaneswar, Odisha 751 003, India
| | - Snehasish Mishra
- School
of Biotechnology, KIIT Deemed-to-be-University, Campus-11, Bhubaneswar, Odisha 751
024, India
| | - Mohammed Aljeldah
- Department
of Clinical Laboratory Sciences, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin 39831, Saudi Arabia
| | - Mohammed Alissa
- Department
of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Mustafa A. Najim
- Department
of Medical Laboratories Technology, College of Applied Medical Sciences, Taibah University, Madinah 41411, Saudi Arabia
| | - Amer Alshengeti
- Department
of Pediatrics, College of Medicine, Taibah
University, Al-Madinah 41491, Saudi Arabia
- Department
of Infection prevention and control, Prince
Mohammad Bin Abdulaziz Hospital, National Guard Health Affairs, Al-Madinah 41491, Saudi Arabia
| | - Bashayer M. AlShehail
- Pharmacy
Practice Department, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Mohammed Garout
- Department
of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia
| | - Muhammad A. Halwani
- Department
of Medical Microbiology, Faculty of Medicine, Al Baha University, Al Baha 4781, Saudi Arabia
| | - Ahmad A. Alshehri
- Department
of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia
| | - Ali A. Rabaan
- Molecular
Diagnostic Laboratory, Johns Hopkins Aramco
Healthcare, Dhahran 31311, Saudi Arabia
- College
of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- Department
of Public Health and Nutrition, The University
of Haripur, Haripur 22610, Pakistan
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5
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Lee MH, Shomanov A, Kudaibergenova M, Viderman D. Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review. J Clin Med 2023; 12:jcm12103446. [PMID: 37240552 DOI: 10.3390/jcm12103446] [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: 02/24/2023] [Revised: 04/25/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.
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Affiliation(s)
- Min-Ho Lee
- School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
| | - Adai Shomanov
- School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
| | - Madina Kudaibergenova
- School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
| | - Dmitriy Viderman
- School of Medicine, Nazarbayev University, 5/1 Kerey and Zhanibek Khandar Str., Astana 010000, Kazakhstan
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6
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Zhang B, Ming C. Digital Transformation and Open Innovation Planning of Response to COVID-19 Outbreak: A Systematic Literature Review and Future Research Agenda. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2731. [PMID: 36768096 PMCID: PMC9916385 DOI: 10.3390/ijerph20032731] [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: 12/30/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 pandemic highlights the importance of digital technology in a specific region's epidemic prevention and control, and the digital transformation strategy based on the open innovation system is an emerging way to tackle conceivable outbreaks. Based on the bibliometric study of relevant literature data, this paper evaluated the research and development status in this field, and conducted a systematic literature review on the basis of the core articles identified. The results of bibliometric analysis software, including CiteSpace, CitNetExplorer and VOSViewer, showed that the development of relevant research presented rapidity and decentralization, and the evolution process of literature topics further implies the necessity of interdisciplinary and multisectoral collaboration. Furthermore, this paper summarized the specific implementation strategies for constructing an open innovation system, and discussed the role and development plan of digital technology in epidemic prevention and control.
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Affiliation(s)
- Ben Zhang
- Law School, Huazhong University of Science and Technology, Wuhan 430074, China
- Sino-European Institute for Intellectual Property, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Chenxu Ming
- Sino-European Institute for Intellectual Property, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
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7
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Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques. Int J Biomed Imaging 2022; 2022:5318447. [PMID: 36588667 PMCID: PMC9800093 DOI: 10.1155/2022/5318447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/05/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.
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8
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Singh R, Tiwari P, Band SS, Rehman AU, Mahajan S, Ding Y, Liu X, Pandit AK. Impact of quarantine on fractional order dynamical model of Covid-19. Comput Biol Med 2022; 151:106266. [PMID: 36395591 PMCID: PMC9660264 DOI: 10.1016/j.compbiomed.2022.106266] [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: 08/29/2022] [Revised: 10/12/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022]
Abstract
In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are evaluated through the fixed point theorem. The Ulam-Hyers stability analysis of the suggested system is established. The two-step Adams-Bashforth-Moulton (ABM) numerical method is employed to find its numerical solution. The numerical simulation is performed to accesses the impact of various biological parameters on the dynamics of Covid-19 disease.
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Affiliation(s)
- Ram Singh
- Baba Ghulam Shah Badshah University Rajouri, 185234, India,Corresponding authors
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden,Corresponding authors
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan, ROC,Corresponding authors
| | | | - Shubham Mahajan
- School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, 182320, India,Ajeenka D Y University, Pune, Maharashtra, India,iNurture Education Solutions Pvt. Ltd., Bangalore, India
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China
| | - Xiaobin Liu
- Department of Nephrology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, 214023, Wuxi, China,Corresponding authors
| | - Amit Kant Pandit
- School of Electronic and Communication, Shri Mata Vaishno Devi University, Katra, 182320, India
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9
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Sitaula C, Shahi TB. Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches. J Med Syst 2022; 46:78. [PMID: 36201085 PMCID: PMC9535233 DOI: 10.1007/s10916-022-01868-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 09/22/2022] [Indexed: 11/17/2022]
Abstract
Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.
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Affiliation(s)
- Chiranjibi Sitaula
- Department of Electrical and Computer Systems Engineering, Monash University, Wellignton Rd, Clayton, VIC, 3800, Australia.
| | - Tej Bahadur Shahi
- School of Engineering and Technology, Central Queensland University, Norman Garden, QLD, 4701, Australia.,Central Department of Computer Science and IT, Tribhuvan University, TU Rd, Kirtipur, Kathmandu, 44618, Nepal
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10
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Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT. Sci Rep 2022; 12:14184. [PMID: 35986073 PMCID: PMC9391448 DOI: 10.1038/s41598-022-18535-8] [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: 02/14/2022] [Accepted: 08/16/2022] [Indexed: 11/14/2022] Open
Abstract
Neck contrast-enhanced CT (CECT) is a routine tool used to evaluate patients with cervical lymphadenopathy. This study aimed to evaluate the ability of convolutional neural networks (CNNs) to classify Kikuchi-Fujimoto’s disease (KD) and cervical tuberculous lymphadenitis (CTL) on neck CECT in patients with benign cervical lymphadenopathy. A retrospective analysis of consecutive patients with biopsy-confirmed KD and CTL in a single center, from January 2012 to June 2020 was performed. This study included 198 patients of whom 125 patients (mean age, 25.1 years ± 8.7, 31 men) had KD and 73 patients (mean age, 41.0 years ± 16.8, 34 men) had CTL. A neuroradiologist manually labelled the enlarged lymph nodes on the CECT images. Using these labels as the reference standard, a CNNs was developed to classify the findings as KD or CTL. The CT images were divided into training (70%), validation (10%), and test (20%) subsets. As a supervised augmentation method, the Cut&Remain method was applied to improve performance. The best area under the receiver operating characteristic curve for classifying KD from CTL for the test set was 0.91. This study shows that the differentiation of KD from CTL on neck CECT using a CNNs is feasible with high diagnostic performance.
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11
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Hu Y, Subagdja B, Tan AH, Quek C, Yin Q. Who are the ‘silent spreaders’?: contact tracing in spatio-temporal memory models. Neural Comput Appl 2022; 34:14859-14879. [PMID: 35599972 PMCID: PMC9107326 DOI: 10.1007/s00521-022-07210-8] [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: 10/26/2021] [Accepted: 03/29/2022] [Indexed: 11/25/2022]
Abstract
The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.
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12
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Aruleba RT, Adekiya TA, Ayawei N, Obaido G, Aruleba K, Mienye ID, Aruleba I, Ogbuokiri B. COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods. Bioengineering (Basel) 2022; 9:153. [PMID: 35447713 PMCID: PMC9024895 DOI: 10.3390/bioengineering9040153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 12/15/2022] Open
Abstract
As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.
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Affiliation(s)
- Raphael Taiwo Aruleba
- Department of Molecular and Cell Biology, Faculty of Science, University of Cape Town, Cape Town 7701, South Africa;
| | - Tayo Alex Adekiya
- Department of Pharmacy and Pharmacology, School of Therapeutic Science, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa;
| | - Nimibofa Ayawei
- Department of Chemistry, Bayelsa Medical University, Yenagoa PMB 178, Bayelsa State, Nigeria;
| | - George Obaido
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Kehinde Aruleba
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Ibomoiye Domor Mienye
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Idowu Aruleba
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Blessing Ogbuokiri
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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Shankar K, Perumal E, Díaz VG, Tiwari P, Gupta D, Saudagar AKJ, Muhammad K. An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images. Appl Soft Comput 2021; 113:107878. [PMID: 34512217 PMCID: PMC8423750 DOI: 10.1016/j.asoc.2021.107878] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/20/2021] [Accepted: 09/02/2021] [Indexed: 12/18/2022]
Abstract
In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.
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Affiliation(s)
- K Shankar
- Federal University of Piauí, Teresina 64049-550, Brazil
| | - Eswaran Perumal
- Department of Computer Applications, Alagappa University, Karaikudi, India
| | - Vicente García Díaz
- Department of Computer Science, School of Computer Science Engineering, University of Oviedo, Spain
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Finland
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, New Delhi, India
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul 143-747, Republic of Korea
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15
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Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques. SECURITY AND COMMUNICATION NETWORKS 2021. [DOI: 10.1155/2021/9293877] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.
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