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Ekinci E, Garip Z, Serbest K. Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms. Comput Biol Med 2024; 178:108812. [PMID: 38943945 DOI: 10.1016/j.compbiomed.2024.108812] [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: 03/15/2024] [Revised: 06/19/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024]
Abstract
The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for determining joint moments often involve sensors or complex mathematical approaches, posing limitations in terms of movement restrictions or expertise requirements. Machine learning (ML) algorithms have emerged as promising tools for joint moment estimation, but the challenge lies in efficiently selecting relevant features from diverse datasets, especially in clinical research settings. This study aims to address this challenge by leveraging metaheuristic optimization algorithms to predict joint moments during STS using minimal input data. Motion analysis data from 20 participants with varied mass and inertia properties are utilized, and joint angles are computed alongside simulations of joint moments. Feature selection is performed using the Manta Ray Foraging Optimization (MRFO), Marine Predators Algorithm (MPA), and Equilibrium Optimizer (EO) algorithms. Subsequently, Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Tree Regression (ETR), and eXtreme Gradient Boosting Regression (XGBoost Regression) ML algorithms are deployed for joint moment prediction. The results reveal EO-ETR as the most effective algorithm for ankle, knee, and neck joint moment prediction, while MPA-ETR exhibits superior performance for hip joint prediction. This approach demonstrates potential for enhancing accuracy in joint moment estimation with minimal feature input, offering implications for biomechanical research and clinical applications.
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Affiliation(s)
- Ekin Ekinci
- Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
| | - Zeynep Garip
- Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
| | - Kasim Serbest
- Department of Mechatronics Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
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Awan T, Khan KB, Mannan A. A compact CNN model for automated detection of COVID-19 using thorax x-ray images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
COVID-19 is an epidemic, causing an enormous death toll. The mutational changing of an RNA virus is causing diagnostic complexities. RT-PCR and Rapid Tests are used for the diagnosis, but unfortunately, these methods are ineffective in diagnosing all strains of COVID-19. There is an utmost need to develop a diagnostic procedure for timely identification. In the proposed work, we come up with a lightweight algorithm based on deep learning to develop a rapid detection system for COVID-19 with thorax chest x-ray (CXR) images. This research aims to develop a fine-tuned convolutional neural network (CNN) model using improved EfficientNetB5. Design is based on compound scaling and trained on the best possible feature extraction algorithm. The low convergence rate of the proposed work can be easily deployed into limited computational resources. It will be helpful for the rapid triaging of victims. 2-fold cross-validation further improves the performance. The algorithm proposed is trained, validated, and testing is performed in the form of internal and external validation on a self-collected and compiled a real-time dataset of CXR. The training dataset is relatively extensive compared to the existing ones. The performance of the proposed technique is measured, validated, and compared with other state-of-the-art pre-trained models. The proposed methodology gives remarkable accuracy (99.5%) and recall (99.5%) for biclassification. The external validation using two different test dataset also give exceptional predictions. The visual depiction of predictions is represented by Grad-CAM maps, presenting the extracted features of the predicted results.
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Affiliation(s)
- Tehreem Awan
- Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
- Department of Electrical Engineering, NFC Institute of Engineering & Technology Multan, Multan, Pakistan
| | - Khan Bahadar Khan
- Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Abdul Mannan
- Department of Electrical Engineering, NFC Institute of Engineering & Technology Multan, Multan, Pakistan
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Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation. J Imaging 2023; 9:jimaging9020042. [PMID: 36826961 PMCID: PMC9963211 DOI: 10.3390/jimaging9020042] [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: 12/20/2022] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection's progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.
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Ullah N, Khan JA, El-Sappagh S, El-Rashidy N, Khan MS. A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network. Diagnostics (Basel) 2023; 13:diagnostics13010162. [PMID: 36611454 PMCID: PMC9818310 DOI: 10.3390/diagnostics13010162] [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: 11/29/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.
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Affiliation(s)
- Naeem Ullah
- Department of Software Engineering, University of Engineering and Technology Taxila, Taxila 47050, Pakistan
| | - Javed Ali Khan
- Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan
- Correspondence: or (J.A.K.); (S.E.-S.)
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
- Correspondence: or (J.A.K.); (S.E.-S.)
| | - Nora El-Rashidy
- Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafr Elsheikh 33516, Egypt
| | - Mohammad Sohail Khan
- Department of Computer Software Engineering, University of Engineering and Technology Mardan, Mardan 23200, Pakistan
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Chen Y, Lin Y, Xu X, Ding J, Li C, Zeng Y, Liu W, Xie W, Huang J. Classification of lungs infected COVID-19 images based on inception-ResNet. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107053. [PMID: 35964421 PMCID: PMC9339166 DOI: 10.1016/j.cmpb.2022.107053] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/18/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival. METHODS The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques. RESULTS The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness. CONCLUSION With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.
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Affiliation(s)
- Yunfeng Chen
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
| | - Yalan Lin
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Xiaodie Xu
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Jinzhen Ding
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Chuzhao Li
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China
| | - Yiming Zeng
- Department of Pulmonary Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
| | - Weili Liu
- Software School, Xinjiang University, Urumqi 830091, China
| | - Weifang Xie
- Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China; Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China; Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University, Quanzhou 362000, China
| | - Jianlong Huang
- Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China; Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China; Key Laboratory of Intelligent Computing and Information Processing, Fujian Province University, Quanzhou 362000, China
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Chakrabartty I, Khan M, Mahanta S, Chopra H, Dhawan M, Choudhary OP, Bibi S, Mohanta YK, Emran TB. Comparative overview of emerging RNA viruses: Epidemiology, pathogenesis, diagnosis and current treatment. Ann Med Surg (Lond) 2022; 79:103985. [PMID: 35721786 PMCID: PMC9188442 DOI: 10.1016/j.amsu.2022.103985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/06/2023] Open
Abstract
From many decades, emerging infections have threatened humanity. The pandemics caused by different CoVs have already claimed and will continue to claim millions of lives. The SARS, Ebola, MERS epidemics and the most recent emergence of COVID-19 pandemic have threatened populations across borders. Since a highly pathogenic CoV has been evolved into the human population in the twenty-first century known as SARS, scientific advancements and innovative methods to tackle these viruses have increased in order to improve response preparedness towards the unpredictable threat posed by these rapidly emerging pathogens. Recently published review articles on SARS-CoV-2 have mainly focused on its pathogenesis, epidemiology and available treatments. However, in this review, we have done a systematic comparison of all three CoVs i.e., SARS, MERS and SARS-CoV-2 along with Ebola and Zika in terms of their epidemiology, virology, clinical features and current treatment strategies. This review focuses on important emerging RNA viruses starting from Zika, Ebola and the CoVs which include SARS, MERS and SARS-CoV-2. Each of these viruses has been elaborated on the basis of their epidemiology, virulence, transmission and treatment. However, special attention has been given to SARS-CoV-2 and the disease caused by it i.e., COVID-19 due to current havoc caused worldwide. At the end, insights into the current understanding of the lessons learned from previous epidemics to combat emerging CoVs have been described. The travel-related viral spread, the unprecedented nosocomial outbreaks and the high case-fatality rates associated with these highly transmissible and pathogenic viruses highlight the need for new prophylactic and therapeutic actions which include but are not limited to clinical indicators, contact tracing, and laboratory investigations as important factors that need to be taken into account in order to arrive at the final conclusion. Recently published review articles on SARS-CoV-2 have mainly focused on its pathogenesis, epidemiology and available treatments. The pandemics caused by different CoVs have already claimed and will continue to claim millions of lives. This review focuses on important emerging RNA viruses starting from Zika, Ebola and the CoVs which include SARS, MERS and SARS-CoV-2. Globally, numerous studies and researchers have recently started fighting this virus.
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Affiliation(s)
- Ishani Chakrabartty
- Department of Applied Biology, School of Biological Sciences, University of Science and Technology Meghalaya (USTM), 9th Mile, Techno City, Baridua, Ri-Bhoi 793101, Meghalaya, India
| | - Maryam Khan
- Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, 202002, U.P, India
| | - Saurov Mahanta
- National Institute of Electronics and Information Technology (NIELIT), Guwahati Centre Guwahati, 781008, Assam, India
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Manish Dhawan
- Department of Microbiology, Punjab Agricultural University, Ludhiana, 141004, Punjab, India.,Trafford College, Altrincham, Manchester, WA14 5PQ, UK
| | - Om Prakash Choudhary
- Department of Veterinary Anatomy and Histology, College of Veterinary Sciences and Animal Husbandry, Central Agricultural University (I), Selesih, Aizawl, India
| | - Shabana Bibi
- Department of Biosciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan.,Yunnan Herbal Laboratory, College of Ecology and Environmental Sciences, Yunnan University, Kunming, 650091, China
| | - Yugal Kishore Mohanta
- Department of Applied Biology, School of Biological Sciences, University of Science and Technology Meghalaya (USTM), 9th Mile, Techno City, Baridua, Ri-Bhoi 793101, Meghalaya, India
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, 4381, Bangladesh.,Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh
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Abedin MJ, Khandaker MU, Uddin MR, Karim MR, Uddin Ahamad MS, Islam MA, Arif AM, Minhaz Hossain SM, Sulieman A, Idris AM. Amassing the Covid-19 driven PPE wastes in the dwelling environment of Chittagong Metropolis and associated implications. CHEMOSPHERE 2022; 297:134022. [PMID: 35202672 PMCID: PMC8859812 DOI: 10.1016/j.chemosphere.2022.134022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 05/22/2023]
Abstract
This study investigates the Covid-19 driven indiscriminate disposal of PPE wastes (mostly face mask and medical wastes) in Chittagong metropolitan area (CMA), Bangladesh. Based on the field monitoring, the mean PPE density (PPE/m2± SD) was calculated to be 0.0226 ± 0.0145, 0.0164 ± 0.0122, and 0.0110 ± 0.00863 for July, August, and September 2021, respectively (during the peak time of Covid-19 in Bangladesh). Moreover, gross information on PPE waste generation in the city was calculated using several parameters such as population density, face mask acceptance rate by urban population, total Covid-19 confirmed cases, quarantined and isolated patients, corresponding medical waste generation rate (kg/bed/day), etc. Moreover, the waste generated due to face mask and other PPEs in the CMA during the whole Covid-19 period (April 4, 2020 to September 5, 2021) were calculated to be 64183.03 and 128695.75 tons, respectively. It has been observed that the negligence of general people, lack of awareness about environmental pollution, and poor municipal waste management practices are the root causes for the contamination of the dwelling environment by PPE wastes. As a result, new challenges have emerged in solid waste management, which necessitates the development of an appropriate waste management strategy. The ultimate policies and strategies may help to achieve the SDG goals 3, 6, 11, 12, 13, and 15, and increase public perception on the use and subsequent disposal of PPEs, especially face masks.
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Affiliation(s)
- Md Jainal Abedin
- Faculty of Public Health, Thammasat University, Pathum Thani, 12121, Thailand
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, 47500, Bandar Sunway, Selangor, Malaysia.
| | - Md Ripaj Uddin
- Institute of National Analytical Research and Service (INARS), BCSIR, Dhanmondi, Dhaka, 1205, Bangladesh
| | - Md Rezaul Karim
- Department of Chemistry, Chittagong University of Engineering and Technology, Chattogram, 4349, Bangladesh
| | | | - Md Ariful Islam
- Department of Chemistry, Chittagong University of Engineering and Technology, Chattogram, 4349, Bangladesh
| | - Abu Mohammad Arif
- One Health Institute, Chattogram Veterinary and Animal Sciences University, Chattogram, 4225, Bangladesh
| | - Syed Md Minhaz Hossain
- Department of Computer Science and Engineering, Premier University, Chattogram, 4000, Bangladesh
| | - A Sulieman
- Department of Radiology and Medical Imaging, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, P.O. Box 422, Alkharj, 11942, Saudi Arabia
| | - Abubakr M Idris
- Department of Chemistry, College of Science, King Khalid University, 62529, Abha, Saudi Arabia; Research Center for Advanced Materials Science (RCAMS), King Khalid University, 62529, Abha, Saudi Arabia
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