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Rajendran S, Panneerselvam RK, Kumar PJ, Rajasekaran VA, Suganya P, Mathivanan SK, Jayagopal P. Prescreening and Triage of COVID-19 Patients Through Chest X-Ray Images Using Deep Learning Model. BIG DATA 2023; 11:408-419. [PMID: 36103285 DOI: 10.1089/big.2022.0028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Deep learning models deliver a fast diagnosis during triage prescreening for COVID-19 patients, reducing waiting time for hospital admission during health emergency scenarios. The Ministry of health and family welfare government of India provides guidelines from the Indian Council of Medical Research (ICMR) for triage requirements and emergency response with faster allotment of oxygen beds for COVID-19 patients requiring immediate treatment in Tamil Nadu, India. A combination of pretrained models provides a faster screening rate and finds patients with severe lung infections who need to be attended to and allotted oxygen beds. Deep learning (DL) algorithms need to be accurate in triaging undifferentiated patients entering the emergency care system (ECS). The major goal of this work is to analyze the accuracy of machine learning approaches in their application to triage the acuity of patients arriving in the ECS. The proposed triage model has an accuracy of 93% in classifying COVID/non-COVID patients. The proposed triage DL model effectively reduces the time for the triage procedure and streamlines screening and allocation of beds for patients with high risk.
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Affiliation(s)
- Sukumar Rajendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | | | | | - Vijay Anand Rajasekaran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Pandy Suganya
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Sandeep Kumar Mathivanan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Prabhu Jayagopal
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
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Ahmed ST, Basha SM, Venkatesan M, Mathivanan SK, Mallik S, Alsubaie N, Alqahtani MS. TVFx - CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique. BMC Med Imaging 2023; 23:146. [PMID: 37784025 PMCID: PMC10544389 DOI: 10.1186/s12880-023-01100-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/11/2023] [Indexed: 10/04/2023] Open
Abstract
COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets.
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Affiliation(s)
- Syed Thouheed Ahmed
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad., Hyderabad, India
- School of Computer Science and Engineering, REVA University, Bengaluru, India
| | - Syed Muzamil Basha
- School of Computer Science and Engineering, REVA University, Bengaluru, India
| | - Muthukumaran Venkatesan
- Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, 603203, India
| | - Sandeep Kumar Mathivanan
- School of Computing Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
- Department of Pharmacology & Toxicology, The University of Arizona, Tucson, AZ, 85721, USA.
| | - Najah Alsubaie
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421, Saudi Arabia
- BioImaging Unit, Space Research Centre, University of Leicester, Michael Atiyah Building, Leicester, LE1 7RH, UK
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Rafique Q, Rehman A, Afghan MS, Ahmad HM, Zafar I, Fayyaz K, Ain Q, Rayan RA, Al-Aidarous KM, Rashid S, Mushtaq G, Sharma R. Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations. Comput Biol Med 2023; 163:107191. [PMID: 37354819 PMCID: PMC10281043 DOI: 10.1016/j.compbiomed.2023.107191] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/28/2023] [Accepted: 06/19/2023] [Indexed: 06/26/2023]
Abstract
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detection. In this study, we explore the realistic development of DL-driven COVID-19 detection methods and focus on the fully automatic framework using available resources, which can effectively investigate various coronavirus variants through modalities. We conducted an exploration and comparison of several diagnostic techniques that are widely used and globally validated for the detection of COVID-19. Furthermore, we explore review-based studies that provide detailed information on synergistic medicine combinations for the treatment of COVID-19. We recommend DL methods that effectively reduce time, cost, and complexity, providing valuable guidance for utilizing available synergistic combinations in clinical and research settings. This study also highlights the implication of innovative diagnostic technical and instrumental strategies, exploring public datasets, and investigating synergistic medicines using optimised DL rules. By summarizing these findings, we aim to assist future researchers in their endeavours by providing a comprehensive overview of the implication of DL techniques in COVID-19 detection and treatment. Integrating DL methods with various diagnostic approaches holds great promise in improving the accuracy and efficiency of COVID-19 diagnostics, thus contributing to effective control and management of the ongoing pandemic.
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Affiliation(s)
- Qandeel Rafique
- Department of Internal Medicine, Sahiwal Medical College, Sahiwal, 57040, Pakistan.
| | - Ali Rehman
- Department of General Medicine Govt. Eye and General Hospital Lahore, 54000, Pakistan.
| | - Muhammad Sher Afghan
- Department of Internal Medicine District Headquarter Hospital Faislaabad, 62300, Pakistan.
| | - Hafiz Muhamad Ahmad
- Department of Internal Medicine District Headquarter Hospital Bahawalnagar, 62300, Pakistan.
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University Pakistan, 44000, Pakistan.
| | - Kompal Fayyaz
- Department of National Centre for Bioinformatics, Quaid-I-Azam University Islamabad, 45320, Pakistan.
| | - Quratul Ain
- Department of Chemistry, Government College Women University Faisalabad, 03822, Pakistan.
| | - Rehab A Rayan
- Department of Epidemiology, High Institute of Public Health, Alexandria University, 21526, Egypt.
| | - Khadija Mohammed Al-Aidarous
- Department of Computer Science, College of Science and Arts in Sharurah, Najran University, 51730, Saudi Arabia.
| | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi Arabia.
| | - Gohar Mushtaq
- Center for Scientific Research, Faculty of Medicine, Idlib University, Idlib, Syria.
| | - Rohit Sharma
- Department of Rasashastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India.
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7453935. [PMID: 36590845 PMCID: PMC9803565 DOI: 10.1155/2022/7453935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/26/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Abstract
In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.
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Saravanan S, Kumar VV, Sarveshwaran V, Indirajithu A, Elangovan D, Allayear SM. Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4380901. [PMID: 36277002 PMCID: PMC9586767 DOI: 10.1155/2022/4380901] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 09/29/2023]
Abstract
The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.
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Affiliation(s)
- S. Saravanan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - V. Vinoth Kumar
- Department of Computer Science and Engineering, Jain (Deemed to Be University), Bangalore, India
| | - Velliangiri Sarveshwaran
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India
| | - Alagiri Indirajithu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014 Tamil Nadu, India
| | - D. Elangovan
- Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
| | - Shaikh Muhammad Allayear
- Department of Multimedia and Creative Technology, Daffodil International University, Daffodil Smart City, Khagan, Ashulia, Dhaka, Bangladesh
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Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8571970. [PMID: 36132548 PMCID: PMC9484938 DOI: 10.1155/2022/8571970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/08/2022] [Accepted: 08/24/2022] [Indexed: 11/23/2022]
Abstract
The level of patient's illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.
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Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8722476. [PMID: 36052054 PMCID: PMC9427231 DOI: 10.1155/2022/8722476] [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: 05/22/2022] [Revised: 07/16/2022] [Accepted: 08/01/2022] [Indexed: 11/25/2022]
Abstract
The difficulty or cost of obtaining data or labels in applications like medical imaging has progressed less quickly. If deep learning techniques can be implemented reliably, automated workflows and more sophisticated analysis may be possible in previously unexplored areas of medical imaging. In addition, numerous characteristics of medical images, such as their high resolution, three-dimensional nature, and anatomical detail across multiple size scales, can increase the complexity of their analysis. This study employs multiconvolutional transfer learning (MCTL) for applying deep learning to small medical imaging datasets in an effort to address these issues. Multiconvolutional transfer learning is a model based on transfer learning that enables deep learning with small datasets. In order to learn new features on a smaller target dataset, an initial baseline is used in the transfer learning process. In this study, 3D MRI images of brain tumors are classified using a convolutional autoencoder method. In order to use unenhanced Magnetic Resonance Imaging (MRI) for clinical diagnosis, expensive and invasive contrast-enhancing procedures must be performed. MCTL has been shown to increase accuracy by 1.5%, indicating that small targets are more easily detected with MCTL. This research can be applied to a wide range of medical imaging and diagnostic procedures, including improving the accuracy of brain tumor severity diagnosis through the use of MRI.
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