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Choudhry IA, Qureshi AN, Aurangzeb K, Iqbal S, Alhussein M. Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features. Biomimetics (Basel) 2023; 8:406. [PMID: 37754157 PMCID: PMC10526442 DOI: 10.3390/biomimetics8050406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 08/25/2023] [Accepted: 08/29/2023] [Indexed: 09/28/2023] Open
Abstract
A recently discovered coronavirus (COVID-19) poses a major danger to human life and health across the planet. The most important step in managing and combating COVID-19 is to accurately screen and diagnose affected people. The imaging technology of lung X-ray is a useful imaging identification/detection approach among them. The help of such computer-aided machines and diagnoses to examine lung X-ray images of COVID-19 instances can give supplemental assessment ideas to specialists, easing their workload to some level. The novel concept of this study is a hybridized approach merging pertinent manual features with deep spatial features for the classification of COVID-19. Further, we employed traditional transfer learning techniques in this investigation, utilizing four different pre-trained CNN-based deep learning models, with the Inception model showing a reasonably accurate result and a diagnosis accuracy of 82.17%. We provide a successful diagnostic approach that blends deep characteristics with machine learning classification to further increase clinical performance. It employs a complete diagnostic model. Two datasets were used to test the suggested approach, and it did quite well on several of them. On 1102 lung X-ray scans, the model was originally evaluated. The results of the experiments indicate that the suggested SVM model has a diagnostic accuracy of 95.57%. When compared to the Xception model's baseline, the diagnostic accuracy had risen by 17.58 percent. The sensitivity, specificity, and AUC of the proposed models were 95.37 percent, 95.39%, and 95.77%, respectively. To show the adaptability of our approach, we also verified our proposed model on other datasets. Finally, we arrived at results that were conclusive. When compared to research of a comparable kind, our suggested CNN model has a greater accuracy of classification and diagnostic effectiveness.
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Affiliation(s)
- Imran Arshad Choudhry
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan
| | - Adnan N. Qureshi
- Faculty of Arts, Society and Professional Studies, Newman University, Birmingham B32 3NT, UK;
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (K.A.); (M.A.)
| | - Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (K.A.); (M.A.)
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Wu Y, Qi Q, Qi S, Yang L, Wang H, Yu H, Li J, Wang G, Zhang P, Liang Z, Chen R. Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans. Comput Biol Med 2023; 154:106567. [PMID: 36738705 PMCID: PMC9869624 DOI: 10.1016/j.compbiomed.2023.106567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/30/2022] [Accepted: 01/22/2023] [Indexed: 01/24/2023]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial. METHODS A deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP). RESULTS LinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods. CONCLUSIONS The proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.
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Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Qianqian Qi
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Liming Yang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
| | - Hanlin Wang
- Department of Radiology, General Hospital of the Yangtze River Shipping, Wuhan, China.
| | - Hui Yu
- General Practice Center, The Seventh Affiliated Hospital, Southern Medical University, Guangzhou, China.
| | - Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, China.
| | - Gang Wang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, China.
| | - Ping Zhang
- Department of Pulmonary and Critical Care Medicine, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, China.
| | - Zhenyu Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Rongchang Chen
- Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, China.
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Rahman AU, Saeed M, Saeed MH, Zebari DA, Albahar M, Abdulkareem KH, Al-Waisy AS, Mohammed MA. A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling. Bioengineering (Basel) 2023; 10:bioengineering10020147. [PMID: 36829641 PMCID: PMC9952481 DOI: 10.3390/bioengineering10020147] [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: 12/19/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients' susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.
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Affiliation(s)
- Atiqe Ur Rahman
- Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan
| | - Muhammad Saeed
- Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan
| | - Muhammad Haris Saeed
- Department of Chemistry, University of Management and Technology, Lahore 54000, Pakistan
| | - Dilovan Asaad Zebari
- Department of Computer Science, College of Science, Nawroz University, Duhok 42001, Iraq
| | - Marwan Albahar
- School of Computer Science, Umm Al Qura University, Mecca 24211, Saudi Arabia
- Correspondence: (M.A.); (M.A.M.)
| | | | - Alaa S. Al-Waisy
- Computer Technologies Engineering Department, Information Technology College, Imam Ja’afar Al-Sadiq University, Baghdad 10001, Iraq
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
- Correspondence: (M.A.); (M.A.M.)
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Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images. ACTA INFORMATICA PRAGENSIA 2023. [DOI: 10.18267/j.aip.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023] Open
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Patro KK, Allam JP, Hammad M, Tadeusiewicz R, Pławiak P. SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19. Biocybern Biomed Eng 2023; 43:352-368. [PMID: 36819118 PMCID: PMC9928742 DOI: 10.1016/j.bbe.2023.01.005] [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: 10/25/2022] [Revised: 12/21/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023]
Abstract
Background and Objective The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems. Methods Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script's title, "SCovNet" refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets. Results A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074. Conclusions The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.
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Affiliation(s)
- Kiran Kumar Patro
- Department of ECE, Aditya Institute of Technology and Management, Tekkali AP-532201, India
| | - Jaya Prakash Allam
- Department of EC, National Institute of Technology Rourkela, Rourkela, Odisha 769008, India
| | - Mohamed Hammad
- Information Technology Dept., Faculty of Computers and Information, Menoufia University, Menoufia, Egypt
| | - Ryszard Tadeusiewicz
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
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Kapoor NR, Kumar A, Kumar A, Zebari DA, Kumar K, Mohammed MA, Al-Waisy AS, Albahar MA. Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16862. [PMID: 36554744 PMCID: PMC9779012 DOI: 10.3390/ijerph192416862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.
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Affiliation(s)
- Nishant Raj Kapoor
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Architecture and Planning Department, CSIR-Central Building Research Institute, Roorkee 247667, India
| | - Ashok Kumar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Architecture and Planning Department, CSIR-Central Building Research Institute, Roorkee 247667, India
| | - Anuj Kumar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Building Energy Efficiency Division, CSIR-Central Building Research Institute, Roorkee 247667, India
| | - Dilovan Asaad Zebari
- Department of Computer Science, College of Science, Nawroz University, Duhok 42001, Iraq
| | - Krishna Kumar
- Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
| | - Alaa S. Al-Waisy
- Computer Technologies Engineering Department, Information Technology College, Imam Ja’afar Al-Sadiq University, Baghdad 10064, Iraq
| | - Marwan Ali Albahar
- School of Computer Science, Umm Al-Qura University, Mecca 24382, Saudi Arabia
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Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1658615. [PMID: 36507230 PMCID: PMC9729025 DOI: 10.1155/2022/1658615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/01/2022] [Accepted: 10/25/2022] [Indexed: 12/02/2022]
Abstract
Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.
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MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches. Bioengineering (Basel) 2022; 9:bioengineering9090457. [PMID: 36135003 PMCID: PMC9495842 DOI: 10.3390/bioengineering9090457] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/02/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022] Open
Abstract
Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those who are at risk of serious disease. Prioritizing a patient with COVID-19 depends on a variety of examination criteria, but due to the large number of these biomarkers, it may be hard for medical practitioners and emergency systems to decide which cases should be given priority for treatment. The aim of this paper is to propose a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients on the basis of combined multi-criteria decision-making (MCDM) methods. In contrast to the existing literature, the MEF has not considered only a single dimension of the examination factors; instead, the proposed framework included different multidimensional examination criteria such as demographic, laboratory findings, vital signs, symptoms, and chronic conditions. A real dataset that consists of data from 78 patients with different examination criteria was used as a base in the construction of Multidimensional Evaluation Matrix (MEM). The proposed framework employs the CRITIC (CRiteria Importance Through Intercriteria Correlation) method to identify objective weights and importance for multidimensional examination criteria. Furthermore, the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is utilized to prioritize COVID-19 severe patients. The results based on the CRITIC method showed that the most important examination criterion for prioritization is COVID-19 patients with heart disease, followed by cough and nasal congestion symptoms. Moreover, the VIKOR method showed that Patients 8, 3, 9, 59, and 1 are the most urgent cases that required the highest priority among the other 78 patients. Finally, the proposed framework can be used by medical organizations to prioritize the most critical COVID-19 patient that has multidimensional examination criteria and to promptly give appropriate care for more precise medicine.
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