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Mahajan HB, Junnarkar AA. Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-24. [PMID: 37362704 PMCID: PMC10105161 DOI: 10.1007/s11042-023-15204-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 08/31/2022] [Accepted: 03/30/2023] [Indexed: 06/28/2023]
Abstract
Cloud-based Healthcare 4.0 systems have research challenges with secure medical data processing, especially biomedical image processing with privacy protection. Medical records are generally text/numerical or multimedia. Multimedia data includes X-ray scans, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, etc. Transferring biomedical multimedia data to medical authorities raises various security concerns. This paper proposes a one-of-a-kind blockchain-based secure biomedical image processing system that maintains anonymity. The integrated Healthcare 4.0 assisted multimedia image processing architecture includes an edge layer, fog computing layer, cloud storage layer, and blockchain layer. The edge layer collects and sends periodic medical information from the patient to the higher layer. The multimedia data from the edge layer is securely preserved in blockchain-assisted cloud storage through fog nodes using lightweight cryptography. Medical users then safely search such data for medical treatment or monitoring. Lightweight cryptographic procedures are proposed by employing Elliptic Curve Cryptography (ECC) with Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Digital Signature (ECDS) algorithm to secure biomedical image processing while maintaining privacy (ECDSA). The proposed technique is experimented with using publically available chest X-ray and CT images. The experimental results revealed that the proposed model shows higher computational efficiency (encryption and decryption time), Peak to Signal Noise Ratio (PSNR), and Meas Square Error (MSE).
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2
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Kaya Y, Gürsoy E. A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection. Soft comput 2023; 27:5521-5535. [PMID: 36618761 PMCID: PMC9812349 DOI: 10.1007/s00500-022-07798-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/24/2022] [Indexed: 01/05/2023]
Abstract
COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model's performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19.
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Affiliation(s)
- Yasin Kaya
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
| | - Ercan Gürsoy
- Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey
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3
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Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic. Soft comput 2023; 27:3427-3442. [PMID: 34421342 PMCID: PMC8371596 DOI: 10.1007/s00500-021-06103-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/30/2021] [Indexed: 12/23/2022]
Abstract
The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.
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4
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Shi Y, Tang A, Xiao Y, Niu L. A lightweight network for COVID-19 detection in X-ray images. Methods 2023; 209:29-37. [PMID: 36460228 PMCID: PMC9706991 DOI: 10.1016/j.ymeth.2022.11.004] [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: 10/16/2022] [Revised: 11/17/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022] Open
Abstract
The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11 × 11 or 3 × 3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method.
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Affiliation(s)
- Yong Shi
- Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Anda Tang
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yang Xiao
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Lingfeng Niu
- Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China,School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China,Corresponding author
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5
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Dweekat OY, Lam SS, McGrath L. Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:796. [PMID: 36613118 PMCID: PMC9819814 DOI: 10.3390/ijerph20010796] [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: 11/18/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients' Electronic Health Records (EHR).
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Affiliation(s)
- Odai Y. Dweekat
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Sarah S. Lam
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
| | - Lindsay McGrath
- Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA
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6
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Bhowal P, Sen S, Sarkar R. A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2023; 14:3659-3674. [PMID: 34567278 PMCID: PMC8455233 DOI: 10.1007/s12652-021-03491-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 08/31/2021] [Indexed: 05/09/2023]
Abstract
The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16, Xception and InceptionV3, where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3. The source code of this work is available at https://github.com/subhankar01/covidfs-aihc.
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Affiliation(s)
- Pratik Bhowal
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India
| | - Subhankar Sen
- Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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7
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Han X, Hu Z, Wang S, Zhang Y. A Survey on Deep Learning in COVID-19 Diagnosis. J Imaging 2022; 9:jimaging9010001. [PMID: 36662099 PMCID: PMC9866755 DOI: 10.3390/jimaging9010001] [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: 11/15/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022] Open
Abstract
According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.
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Affiliation(s)
- Xue Han
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Zuojin Hu
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
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8
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Datta R, Mandal S, Umer S, AlZubi AA, Alharbi A, Alanazi JM. Single-image reconstruction using novel super-resolution technique for large-scaled images. Soft comput 2022; 26:8089-8103. [PMID: 35582159 PMCID: PMC9099350 DOI: 10.1007/s00500-022-07142-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2022] [Indexed: 11/29/2022]
Abstract
A fast and novel method for single-image reconstruction using the super-resolution (SR) technique has been proposed in this paper. The working principle of the proposed scheme has been divided into three components. A low-resolution image is divided into several homogeneous or non-homogeneous regions in the first component. This partition is based on the analysis of texture patterns within that region. Only the non-homogeneous regions undergo the sparse representation for SR image reconstruction in the second component. The obtained reconstructed region from the second component undergoes a statistical-based prediction model to generate its more enhanced version in the third component. The remaining homogeneous regions are bicubic interpolated and reflect the required high-resolution image. The proposed technique is applied to some Large-scale electrical, machine and civil architectural design images. The purpose of using these images is that these images are huge in size, and processing such large images for any application is time-consuming. The proposed SR technique results in a better reconstructed SR image from its lower version with low time complexity. The performance of the proposed system on the electrical, machine and civil architectural design images is compared with the state-of-the-art methods, and it is shown that the proposed scheme outperforms the other competing methods.
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Affiliation(s)
- Ramanath Datta
- Department of Electronics and Communication Engineering, St.Thomas’ College of Engineering and Technology, Kolkata, India
| | - Sekhar Mandal
- Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Kolkata, India
| | - Saiyed Umer
- Department of Computer Science and Engineering, Aliah University, Kolkata, India
| | - Ahmad Ali AlZubi
- Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia
| | - Abdullah Alharbi
- Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia
| | - Jazem Mutared Alanazi
- Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia
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9
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Riaz M, Bashir M, Younas I. Metaheuristics based COVID-19 detection using medical images: A review. Comput Biol Med 2022; 144:105344. [PMID: 35294913 PMCID: PMC8907145 DOI: 10.1016/j.compbiomed.2022.105344] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 02/21/2022] [Accepted: 02/21/2022] [Indexed: 01/02/2023]
Abstract
Many countries in the world have been facing the rapid spread of COVID-19 since February 2020. There is a dire need for efficient and cheap automated diagnosis systems that can reduce the pressure on healthcare systems. Extensive research is being done on the use of image classification for the detection of COVID-19 through X-ray and CT-scan images of patients. Deep learning has been the most popular technique for image classification during the last decade. However, the performance of deep learning-based methods heavily depends on the architecture of the deep neural network. Over the last few years, metaheuristics have gained popularity for optimizing the architecture of deep neural networks. Metaheuristics have been widely used to solve different complex non-linear optimization problems due to their flexibility, simplicity, and problem independence. This paper aims to study the different image classification techniques for chest images, including the applications of metaheuristics for optimization and feature selection of deep learning and machine learning models. The motivation of this study is to focus on applications of different types of metaheuristics for COVID-19 detection and to shed some light on future challenges in COVID-19 detection from medical images. The aim is to inspire researchers to focus their research on overlooked aspects of COVID-19 detection.
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Affiliation(s)
| | - Maryam Bashir
- FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan.
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10
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Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3019194. [PMID: 35463246 PMCID: PMC9023189 DOI: 10.1155/2022/3019194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/17/2022] [Accepted: 01/29/2022] [Indexed: 12/01/2022]
Abstract
A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.
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11
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Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2696916. [PMID: 35411308 PMCID: PMC8994690 DOI: 10.1155/2022/2696916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/04/2022] [Accepted: 03/08/2022] [Indexed: 12/31/2022]
Abstract
Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.
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12
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Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9009406. [PMID: 35368938 PMCID: PMC8968355 DOI: 10.1155/2022/9009406] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/27/2021] [Accepted: 01/11/2022] [Indexed: 11/18/2022]
Abstract
This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person’s life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1–F7), multimodal (F8–F13), and fixed-dimension multimodal (F14–F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu’s maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.
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13
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Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7377502. [PMID: 35280708 PMCID: PMC8896964 DOI: 10.1155/2022/7377502] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/24/2022] [Indexed: 12/17/2022]
Abstract
Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.
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14
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Malhotra P, Gupta S, Koundal D, Zaguia A, Kaur M, Lee HN. Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:2278. [PMID: 35336449 PMCID: PMC8955356 DOI: 10.3390/s22062278] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/21/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Pneumothorax is a thoracic disease leading to failure of the respiratory system, cardiac arrest, or in extreme cases, death. Chest X-ray (CXR) imaging is the primary diagnostic imaging technique for the diagnosis of pneumothorax. A computerized diagnosis system can detect pneumothorax in chest radiographic images, which provide substantial benefits in disease diagnosis. In the present work, a deep learning neural network model is proposed to detect the regions of pneumothoraces in the chest X-ray images. The model incorporates a Mask Regional Convolutional Neural Network (Mask RCNN) framework and transfer learning with ResNet101 as a backbone feature pyramid network (FPN). The proposed model was trained on a pneumothorax dataset prepared by the Society for Imaging Informatics in Medicine in association with American college of Radiology (SIIM-ACR). The present work compares the operation of the proposed MRCNN model based on ResNet101 as an FPN with the conventional model based on ResNet50 as an FPN. The proposed model had lower class loss, bounding box loss, and mask loss as compared to the conventional model based on ResNet50 as an FPN. Both models were simulated with a learning rate of 0.0004 and 0.0006 with 10 and 12 epochs, respectively.
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Affiliation(s)
- Priyanka Malhotra
- Chitkara University Institute of Engineering and Technology, Chitkara University, Patiala 140401, Punjab, India; (P.M.); (S.G.)
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Patiala 140401, Punjab, India; (P.M.); (S.G.)
| | - Deepika Koundal
- Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India;
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Manjit Kaur
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
| | - Heung-No Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
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15
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Singh A, Krishna Raguru J, Prasad G, Chauhan S, Tiwari PK, Zaguia A, Ullah MA. Medical Image Captioning Using Optimized Deep Learning Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9638438. [PMID: 35341200 PMCID: PMC8947912 DOI: 10.1155/2022/9638438] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/03/2021] [Accepted: 01/21/2022] [Indexed: 11/25/2022]
Abstract
Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.
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Affiliation(s)
- Arjun Singh
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | | | - Gaurav Prasad
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Surbhi Chauhan
- Computer Science and Engineering, Jaipur Institute of Engineering and Management, Jaipur, India
| | | | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Mohammad Aman Ullah
- Department of Computer Science and Engineering, International Islamic University, Chittagong, Bangladesh
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16
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Malche T, Tharewal S, Tiwari PK, Jabarulla MY, Alnuaim AA, Hatamleh WA, Ullah MA. Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8732213. [PMID: 35273786 PMCID: PMC8904099 DOI: 10.1155/2022/8732213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/17/2022] [Accepted: 02/09/2022] [Indexed: 11/17/2022]
Abstract
Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.
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Affiliation(s)
| | - Sumegh Tharewal
- School of Computer Science, Dr. Vishwanath Karad MIT World peace University, S. No.124, Paud Road, Kothrud, Pune 411038, Maharashtra, India
| | | | - Mohamed Yaseen Jabarulla
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. BOX 22459, Riyadh 11495, Saudi Arabia
| | - Wesam Atef Hatamleh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
| | - Mohammad Aman Ullah
- Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong, Bangladesh
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17
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An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2933015. [PMID: 35265109 PMCID: PMC8901315 DOI: 10.1155/2022/2933015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 01/26/2022] [Accepted: 02/07/2022] [Indexed: 12/05/2022]
Abstract
Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve.
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18
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Meivel S, Sindhwani N, Anand R, Pandey D, Alnuaim AA, Altheneyan AS, Jabarulla MY, Lelisho ME. Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2103975. [PMID: 35116063 PMCID: PMC8804552 DOI: 10.1155/2022/2103975] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/30/2021] [Accepted: 01/03/2022] [Indexed: 02/03/2023]
Abstract
The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.
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Affiliation(s)
- S. Meivel
- M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
| | | | - Rohit Anand
- DSEU, G. B. Pant Okhla-1 Campus, New Delhi, India
| | - Digvijay Pandey
- Department of Technical Education, IET Lucknow, Dr. A. P. J Abdul Kalam Technical University Lucknow, Lucknow, India
| | - Abeer Ali Alnuaim
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi Arabia
| | - Alaa S. Altheneyan
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi Arabia
| | - Mohamed Yaseen Jabarulla
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Republic of Korea
| | - Mesfin Esayas Lelisho
- Department of Statistics, College of Natural and Computational Science, Mizan-Tepi University, Tepi, Ethiopia
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19
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Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4130674. [PMID: 35178226 PMCID: PMC8846984 DOI: 10.1155/2022/4130674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/09/2021] [Accepted: 12/15/2021] [Indexed: 12/11/2022]
Abstract
Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.
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20
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Data Security-Based Routing in MANETs Using Key Management Mechanism. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031041] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
A Mobile Ad Hoc Network (MANET) is an autonomous network developed using wireless mobile nodes without the support of any kind of infrastructure. In a MANET, nodes can communicate with each other freely and dynamically. However, MANETs are prone to serious security threats that are difficult to resist using the existing security approaches. Therefore, various secure routing protocols have been developed to strengthen the security of MANETs. In this paper, a secure and energy-efficient routing protocol is proposed by using group key management. Asymmetric key cryptography is used, which involves two specialized nodes, labeled the Calculator Key (CK) and the Distribution Key (DK). These two nodes are responsible for the generation, verification, and distribution of secret keys. As a result, other nodes need not perform any kind of additional computation for building the secret keys. These nodes are selected using the energy consumption and trust values of nodes. In most of the existing routing protocols, each node is responsible for the generation and distribution of its own secret keys, which results in more energy dissemination. Moreover, if any node is compromised, security breaches should occur. When nodes other than the CK and DK are compromised, the entire network’s security is not jeopardized. Extensive experiments are performed by considering the existing and the proposed protocols. Performance analyses reveal that the proposed protocol outperforms the competitive protocols.
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21
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Jalali Moghaddam M, Ghavipour M. Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging. IPEM-TRANSLATION 2022; 3:100008. [PMID: 36312890 PMCID: PMC9597575 DOI: 10.1016/j.ipemt.2022.100008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/08/2022]
Abstract
The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.
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22
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Danilov VV, Proutski A, Karpovsky A, Kirpich A, Litmanovich D, Nefaridze D, Talalov O, Semyonov S, Koniukhovskii V, Shvartc V, Gankin Y. Indirect supervision applied to COVID-19 and pneumonia classification. INFORMATICS IN MEDICINE UNLOCKED 2021; 28:100835. [PMID: 34977331 PMCID: PMC8712713 DOI: 10.1016/j.imu.2021.100835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/11/2021] [Accepted: 12/23/2021] [Indexed: 01/08/2023] Open
Abstract
The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.
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Affiliation(s)
- Viacheslav V Danilov
- Tomsk Polytechnic University, Tomsk, Russia
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
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23
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COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311423] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.
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24
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Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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25
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A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9437538. [PMID: 34777739 PMCID: PMC8589496 DOI: 10.1155/2021/9437538] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/26/2021] [Accepted: 10/07/2021] [Indexed: 12/24/2022]
Abstract
COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.
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26
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Mousavi Mojab SZ, Shams S, Fotouhi F, Soltanian-Zadeh H. EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images. Sci Rep 2021; 11:21564. [PMID: 34732741 PMCID: PMC8566470 DOI: 10.1038/s41598-021-00524-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/24/2021] [Indexed: 01/29/2023] Open
Abstract
The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.
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Affiliation(s)
- Seyed Ziae Mousavi Mojab
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI, USA.
| | - Seyedmohammad Shams
- Medical Image Analysis Lab, Department of Radiology, Henry Ford Health System, Detroit, MI, USA
| | - Farshad Fotouhi
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI, USA
| | - Hamid Soltanian-Zadeh
- Medical Image Analysis Lab, Department of Radiology, Henry Ford Health System, Detroit, MI, USA
- CIPCE, Department of ECE, College of Engineering, University of Tehran, Tehran, Iran
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27
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Khan MA, Alhaisoni M, Tariq U, Hussain N, Majid A, Damaševičius R, Maskeliūnas R. COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion. SENSORS (BASEL, SWITZERLAND) 2021; 21:7286. [PMID: 34770595 PMCID: PMC8588229 DOI: 10.3390/s21217286] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022]
Abstract
In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach-parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.
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Affiliation(s)
- Muhammad Attique Khan
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan; (M.A.K.); (N.H.); (A.M.)
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia;
| | - Usman Tariq
- Information Systems Department, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Khraj 11942, Saudi Arabia;
| | - Nazar Hussain
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan; (M.A.K.); (N.H.); (A.M.)
| | - Abdul Majid
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan; (M.A.K.); (N.H.); (A.M.)
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania;
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28
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Zhang Z, Chen B, Sun J, Luo Y. A bagging dynamic deep learning network for diagnosing COVID-19. Sci Rep 2021; 11:16280. [PMID: 34381079 PMCID: PMC8358001 DOI: 10.1038/s41598-021-95537-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 07/26/2021] [Indexed: 01/19/2023] Open
Abstract
COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.
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Affiliation(s)
- Zhijun Zhang
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, 510335, China.
- School of Automation Science and Engineering, East China Jiaotong University, Nanchang, 330052, China.
- Shaanxi Provincial Key Laboratory of Industrial Automation, School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China.
- School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China.
| | - Bozhao Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Jiansheng Sun
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Yamei Luo
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China
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Khanna M, Agarwal A, Singh LK, Thawkar S, Khanna A, Gupta D. Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021; 48:1-33. [PMID: 34395156 PMCID: PMC8349241 DOI: 10.1007/s13369-021-05880-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 06/15/2021] [Indexed: 12/24/2022]
Abstract
COVID-19 is an ongoing pandemic that is widely spreading daily and reaches a significant community spread. X-ray images, computed tomography (CT) images and test kits (RT-PCR) are three easily available options for predicting this infection. Compared to the screening of COVID-19 infection from X-ray and CT images, the test kits(RT-PCR) available to diagnose COVID-19 face problems such as high analytical time, high false negative outcomes, poor sensitivity and specificity. Radiological signatures that X-rays can detect have been found in COVID-19 positive patients. Radiologists may examine these signatures, but it's a time-consuming and error-prone process (riddled with intra-observer variability). Thus, the chest X-ray analysis process needs to be automated, for which AI-driven tools have proven to be the best choice to increase accuracy and speed up analysis time, especially in the case of medical image analysis. We shortlisted four datasets and 20 CNN-based models to test and validate the best ones using 16 detailed experiments with fivefold cross-validation. The two proposed models, ensemble deep transfer learning CNN model and hybrid LSTMCNN, perform the best. The accuracy of ensemble CNN was up to 99.78% (96.51% average-wise), F1-score up to 0.9977 (0.9682 average-wise) and AUC up to 0.9978 (0.9583 average-wise). The accuracy of LSTMCNN was up to 98.66% (96.46% average-wise), F1-score up to 0.9974 (0.9668 average-wise) and AUC up to 0.9856 (0.9645 average-wise). These two best pre-trained transfer learning-based detection models can contribute clinically by offering the patients prediction correctly and rapidly.
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Affiliation(s)
- Munish Khanna
- Hindustan College of Science and Technology, Mathura, 281122 India
| | - Astitwa Agarwal
- Hindustan College of Science and Technology, Mathura, 281122 India
| | - Law Kumar Singh
- Hindustan College of Science and Technology, Mathura, 281122 India
| | - Shankar Thawkar
- Hindustan College of Science and Technology, Mathura, 281122 India
| | - Ashish Khanna
- Maharaja Agrasen Institute of Technology, Delhi, 110034 India
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Delhi, 110034 India
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30
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Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NMAR, Alhaidari FA, Shaikh FS, Alsenbel YM, Alalharith DM, Alharthi HM, Alghamdi WM, Alshahrani MS. Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:102327-102344. [PMID: 34786317 PMCID: PMC8545201 DOI: 10.1109/access.2021.3097559] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/09/2021] [Indexed: 06/02/2023]
Abstract
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
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Affiliation(s)
- Kawther S. Alqudaihi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nida Aslam
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Irfan Ullah Khan
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Abdullah M. Almuhaideb
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Shikah J. Alsunaidi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nehad M. Abdel Rahman Ibrahim
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fahd A. Alhaidari
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fatema S. Shaikh
- Department of Computer Information SystemsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Yasmine M. Alsenbel
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Dima M. Alalharith
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Hajar M. Alharthi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Wejdan M. Alghamdi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Mohammed S. Alshahrani
- Department of Emergency MedicineCollege of MedicineImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
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31
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Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R. Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans. Sci Rep 2021; 11:14133. [PMID: 34238992 PMCID: PMC8266871 DOI: 10.1038/s41598-021-93658-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/16/2021] [Indexed: 12/22/2022] Open
Abstract
COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
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Affiliation(s)
- Rohit Kundu
- Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India
| | - Hritam Basak
- Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Kolkata, 700106, India
| | - Ali Ahmadian
- Institute of IR 4.0, The National University of Malaysia (UKM), 43600, Bangi, Selangor, Malaysia.
- Department of Law, Economics and Human Sciences & Decisions Lab, Mediterranea University of Reggio Calabria, 89125, Reggio Calabria, Italy.
| | - Massimiliano Ferrara
- Department of Law, Economics and Human Sciences & Decisions Lab, Mediterranea University of Reggio Calabria, 89125, Reggio Calabria, Italy
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India
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32
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Mehedi IM, Shah HSM, Al-Saggaf UM, Mansouri R, Bettayeb M. Fuzzy PID Control for Respiratory Systems. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:7118711. [PMID: 34257855 PMCID: PMC8253636 DOI: 10.1155/2021/7118711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/08/2021] [Accepted: 06/12/2021] [Indexed: 01/10/2023]
Abstract
This paper presents the implementation of a fuzzy proportional integral derivative (FPID) control design to track the airway pressure during the mechanical ventilation process. A respiratory system is modeled as a combination of a blower-hose-patient system and a single compartmental lung system with nonlinear lung compliance. For comparison purposes, the classical PID controller is also designed and simulated on the same system. According to the proposed control strategy, the ventilator will provide airway flow that maintains the peak pressure below critical levels when there are unknown parameters of the patient's hose leak and patient breathing effort. Results show that FPID is a better controller in the sense of quicker response, lower overshoot, and smaller tracking error. This provides valuable insight for the application of the proposed controller.
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Affiliation(s)
- Ibrahim M. Mehedi
- Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Heidir S. M. Shah
- Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Ubaid M. Al-Saggaf
- Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Rachid Mansouri
- Laboratoire de Conception et Conduite des Systemes de Production (L2CSP), Tizi Ouzou, Algeria
| | - Maamar Bettayeb
- Electrical Engineering Department, University of Sharjah, Sharjah, UAE
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33
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Mehedi IM, Shah HSM, Al-Saggaf UM, Mansouri R, Bettayeb M. Adaptive Fuzzy Sliding Mode Control of a Pressure-Controlled Artificial Ventilator. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1926711. [PMID: 34257849 PMCID: PMC8249163 DOI: 10.1155/2021/1926711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/08/2021] [Accepted: 06/12/2021] [Indexed: 11/19/2022]
Abstract
This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient's lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.
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Affiliation(s)
- Ibrahim M. Mehedi
- Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Heidir S. M. Shah
- Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Ubaid M. Al-Saggaf
- Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Rachid Mansouri
- Laboratoire de Conception et Conduite des Systemes de Production (L2CSP), Tizi-Ouzou 15000, Algeria
| | - Maamar Bettayeb
- Electrical Engineering Department, University of Sharjah, Sharjah, UAE
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34
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Kumar V, Singh D, Kaur M, Damaševičius R. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Comput Sci 2021; 7:e564. [PMID: 34141890 PMCID: PMC8176528 DOI: 10.7717/peerj-cs.564] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 05/05/2021] [Indexed: 05/09/2023]
Abstract
BACKGROUND Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. METHODOLOGY This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. RESULTS In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. CONCLUSIONS The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.
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Affiliation(s)
- Vijay Kumar
- Computer Science and Engineering Department, National Institute of Technology, Hamirpur, Himachal Pradesh, India
| | - Dilbag Singh
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Manjit Kaur
- School of Engineering and Applied Sciences, Bennett University, Greater Noida, India
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland
- Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania
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35
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Kumar N, Gupta M, Gupta D, Tiwari S. Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 14:469-478. [PMID: 34025813 PMCID: PMC8123104 DOI: 10.1007/s12652-021-03306-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 05/01/2021] [Indexed: 06/12/2023]
Abstract
Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.
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Affiliation(s)
- N. Kumar
- Department of Computer Science & Engineering, Maharaja Surajmal Institute of Technology, C-4, Janakpuri, New Delhi, India
| | - M. Gupta
- Department of Computer Science & Engineering, Moradabad Institute of Technology, Moradabad, India
| | - D. Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
| | - S. Tiwari
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India
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