1
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Qayyum A, Benzinou A, Saidani O, Alhayan F, Khan MA, Masood A, Mazher M. Assessment and classification of COVID-19 DNA sequence using pairwise features concatenation from multi-transformer and deep features with machine learning models. SLAS Technol 2024; 29:100147. [PMID: 38796034 DOI: 10.1016/j.slast.2024.100147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/31/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such a major viral outbreak demands early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. The emerging global infectious COVID-19 disease by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) presents critical threats to global public health and the economy since it was identified in late December 2019 in China. The virus has gone through various pathways of evolution. Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying deep learning and machine learning approaches. In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine and deep learning techniques have been used in recent years to complete this task with some success. The classification of DNA sequences is a key research area in bioinformatics as it enables researchers to conduct genomic analysis and detect possible diseases. In this paper, three state-of-the-art deep learning-based models are proposed using two DNA sequence conversion methods. We also proposed a novel multi-transformer deep learning model and pairwise features fusion technique for DNA sequence classification. Furthermore, deep features are extracted from the last layer of the multi-transformer and used in machine-learning models for DNA sequence classification. The k-mer and one-hot encoding sequence conversion techniques have been presented. The proposed multi-transformer achieved the highest performance in COVID DNA sequence classification. Automatic identification and classification of viruses are essential to avoid an outbreak like COVID-19. It also helps in detecting the effect of viruses and drug design.
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Affiliation(s)
- Abdul Qayyum
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | | | - Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia.
| | - Fatimah Alhayan
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia.
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Anum Masood
- Department of Physics, Norwegian University of Science and Technology, Trondheim NO-7491, Norway.
| | - Moona Mazher
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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2
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Yu M, Xiong Y, Liang P, Chen D, Zhang Y, Liu H, Zhang Y, Zhao X, Jin R, Wang X. Validation of 12 Rapid Antigen Tests for the Detection of SARS-CoV-2. J Biol Methods 2024; 11:e99010009. [PMID: 38988498 PMCID: PMC11231045 DOI: 10.14440/jbm.2024.409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 07/12/2024] Open
Abstract
The rapid identification SARS-CoV-2 virus has become the basis for the control of the COVID-19 outbreak. The rapid antigen tests for SARS-CoV-2 are quick, widely available, and inexpensive. Rapid antigen tests have gradually replaced the time-consuming and costly RT-PCR. Currently, although several RAT kits have been extensively used for the diagnosis of COVID-19, validity data are limited due to the inconsistent sensitivity and poor reproducibility. Meanwhile, WHO does not recommend specific commercial RAT kits. Therefore, it is crucial to establish a method to evaluate the effectiveness of different rapid antigen tests kits. This study aimed to develop an evaluation system for rapid antigen tests to provide an efficient and accurate technique for screening SARS-CoV-2 antigen detection kits. Given large number of rapid antigen tests kits available, this study only focused on those that are representative and commonely used in China. By minimzing biases through randomization, concealment, and blinding, we eventually found that the Test 1 had the lowest sensitivity and the Test VI had the highest sensitivity. This study provided an evaluation platform that can potentially serve as a reference for COVID-19 diagnostic strategies.
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Affiliation(s)
- Minghang Yu
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Yang Xiong
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
| | - Pu Liang
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Danying Chen
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Yuting Zhang
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Huan Liu
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Yuanyuan Zhang
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Xuesen Zhao
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Ronghua Jin
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
| | - Xi Wang
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R.China
- Beijing Institute of Infectious Diseases, Beijing, 100015, P.R.China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing,100015, P.R.China
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3
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Sharma S, Shrivastava S, Kausley SB, Rai B. Integrated point-of-care RT-PCR methods during and after COVID-19 pandemic. Virusdisease 2023; 34:356-364. [PMID: 37780898 PMCID: PMC10533447 DOI: 10.1007/s13337-023-00834-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 08/07/2023] [Indexed: 10/03/2023] Open
Abstract
The COVID-19 pandemic has taken the world by surprise and people and organisations worldwide worked in some way or the other to combat the spread; isolate from the infected and get back to normal life, as it was before the pandemic hit. In this regard, the diagnosis of COVID-19 was at the centre of control and prevention and have seen a vehement change in every aspect, especially development of point-of-care testing for better and quick diagnosis. Among different types of techniques developed, the most important was the RT-PCR method of detection which detects nucleic acid of the virus in samples. RT-PCR is a laboratory-based method requiring trained professionals and precise steps for accurate testing. With the advent and spread of the pandemic, number of RT-PCR diagnostic centres rose significantly, and the detection process became less cumbersome, easy to use, ability to handle large volume of samples, more accurate, less time-consuming, and cost-effective. Different industries developed RT-PCR kits, reducing the efforts to prepare laboratory samples. Machines were employed for labour-driven tasks in PCR testing. In addition, new age technologies such as artificial intelligence, IoT, digital systems were combined with RT-PCR for accurate and easy testing. In this review, point-of-care RT-PCR methods, when the COVID-19 started, and the methods now, has been compared on the basis of technological advancements.
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Affiliation(s)
- Shagun Sharma
- Department of Zoology, University of Rajasthan, JLN Marg, Jaipur, 302004 India
| | - Surabhi Shrivastava
- Physical Sciences Research Area, TCS Research, Tata Research Development and Design Centre, Tata Consultancy Services Limited, Pune, 411013 India
| | - Shankar B. Kausley
- Physical Sciences Research Area, TCS Research, Tata Research Development and Design Centre, Tata Consultancy Services Limited, Pune, 411013 India
| | - Beena Rai
- Physical Sciences Research Area, TCS Research, Tata Research Development and Design Centre, Tata Consultancy Services Limited, Pune, 411013 India
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4
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Joloudari JH, Azizi F, Nodehi I, Nematollahi MA, Kamrannejhad F, Hassannatajjeloudari E, Alizadehsani R, Islam SMS. Developing a Deep Neural Network model for COVID-19 diagnosis based on CT scan images. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:16236-16258. [PMID: 37920011 DOI: 10.3934/mbe.2023725] [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: 11/04/2023]
Abstract
COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.
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Affiliation(s)
| | - Faezeh Azizi
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Issa Nodehi
- Department of Computer Engineering, University of Qom, Qom, Iran
| | | | - Fateme Kamrannejhad
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Edris Hassannatajjeloudari
- Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia
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5
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Hammad MS, Ghoneim VF, Mabrouk MS, Al-Atabany WI. A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques. Sci Rep 2023; 13:4003. [PMID: 36899035 PMCID: PMC9999081 DOI: 10.1038/s41598-023-30941-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 03/03/2023] [Indexed: 03/12/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.
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Affiliation(s)
- Muhammed S Hammad
- Biomedical Engineering Department, Helwan University, Helwan, Egypt.
| | - Vidan F Ghoneim
- Biomedical Engineering Department, Helwan University, Helwan, Egypt
| | - Mai S Mabrouk
- Biomedical Engineering Department, Misr University for Science and Technology (MUST), 6th of October City, Egypt
| | - Walid I Al-Atabany
- Biomedical Engineering Department, Helwan University, Helwan, Egypt.,Center for Informatics Science, Nile University, Sheikh Zayed City, Egypt
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6
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Shirahige L, Leimig B, Baltar A, Bezerra A, de Brito CVF, do Nascimento YSO, Gomes JC, Teo WP, Dos Santos WP, Cairrão M, Fonseca A, Monte-Silva K. Classification of Parkinson's disease motor phenotype: a machine learning approach. J Neural Transm (Vienna) 2022; 129:1447-1461. [PMID: 36335541 DOI: 10.1007/s00702-022-02552-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 10/16/2022] [Indexed: 11/08/2022]
Abstract
To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.
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Affiliation(s)
- Lívia Shirahige
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil.,Post-graduation Program of Neuropsychiatry and Behavioral Sciences, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Brenda Leimig
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil
| | - Adriana Baltar
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil.,Post-graduation Program of Neuropsychiatry and Behavioral Sciences, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Amanda Bezerra
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil
| | | | | | - Juliana Carneiro Gomes
- Department of Biomedical Engineering, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - Wei-Peng Teo
- Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore, Singapore
| | | | - Marcelo Cairrão
- Neurodynamics Laboratory, Department of Physiology, Universidade Federal de Pernambuco, Recife, PE, Brazil
| | - André Fonseca
- Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, São Paulo, São Paulo, Brazil
| | - Kátia Monte-Silva
- Applied Neuroscience Laboratory, Department of Physical Therapy, Universidade Federal de Pernambuco, w/n Jornalista Aníbal Fernandes Avenue, Recife, PE, 50740-560, Brazil.
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7
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Khodaei A, Shams P, Sharifi H, Mozaffari-Tazehkand B. Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods. Biomed Signal Process Control 2022; 80:104192. [PMID: 36168586 PMCID: PMC9500098 DOI: 10.1016/j.bspc.2022.104192] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/12/2022] [Accepted: 09/10/2022] [Indexed: 11/30/2022]
Abstract
Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious viruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.
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Affiliation(s)
- Amin Khodaei
- Faculty of Electrical & Computer Engineering of University of Tabriz, 29 Bahman Blvd, Tabriz, Iran
| | - Parvaneh Shams
- Computer Engineering Department, Istanbul Aydin University, Turkey
| | - Hadi Sharifi
- Faculty of Electrical & Computer Engineering of University of Tabriz, 29 Bahman Blvd, Tabriz, Iran
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8
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MXT: A New Variant of Pyramid Vision Transformer for Multi-label Chest X-ray Image Classification. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10032-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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9
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Álvarez-Rodríguez L, Moura JD, Novo J, Ortega M. Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? BMC Med Res Methodol 2022; 22:125. [PMID: 35484483 PMCID: PMC9046709 DOI: 10.1186/s12874-022-01578-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/21/2022] [Indexed: 11/10/2022] Open
Abstract
Background The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas. Methods The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process. Results The results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude. Conclusions Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario. Supplementary Information The online version contains supplementary material available at (10.1186/s12874-022-01578-w).
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Affiliation(s)
- Lorena Álvarez-Rodríguez
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
| | - Joaquim de Moura
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain. .,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain.
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
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10
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Scarpiniti M, Sarv Ahrabi S, Baccarelli E, Piazzo L, Momenzadeh A. A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection. EXPERT SYSTEMS WITH APPLICATIONS 2022; 192:116366. [PMID: 34937995 PMCID: PMC8675154 DOI: 10.1016/j.eswa.2021.116366] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/15/2021] [Accepted: 11/30/2021] [Indexed: 05/02/2023]
Abstract
Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.
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Affiliation(s)
- Michele Scarpiniti
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Sima Sarv Ahrabi
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Enzo Baccarelli
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
| | - Alireza Momenzadeh
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
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11
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COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network. Health Inf Sci Syst 2022; 10:1. [PMID: 35096384 PMCID: PMC8785028 DOI: 10.1007/s13755-021-00169-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 12/27/2021] [Indexed: 12/25/2022] Open
Abstract
The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.
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Roy I, Shai R, Ghosh A, Bej A, Pati SK. CoWarriorNet: A Novel Deep-Learning Framework for CoVID-19 Detection from Chest X-Ray Images. NEW GENERATION COMPUTING 2022; 40:961-985. [PMID: 34876770 PMCID: PMC8639408 DOI: 10.1007/s00354-021-00143-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/21/2021] [Indexed: 05/17/2023]
Abstract
UNLABELLED Even after scavenging the existence of mankind for the past year, the wrath of CoVID-19 is yet to die down. Countries like India are still getting haunted by the devastating conundrum, with coronavirus ripping through its citizens in the concurrent second wave. The surge of cases has prompted rapid intervention, with medical authorities pushing it to the limit to curve a roadblock to its aggressive growth. But, even after effortless work, human intervention remains slow and insufficient. Furthermore, relevant testing methodologies have shown weakness while detecting threats, with the recent growth of post-Covid complexities, thereby leaving a painful mark. This as such created a major requirement for technological advancements, which can cater to the mass. The growth of computational prowess in the past decade made the field of Deep Learning a major contributor in curving out algorithms to solve this. Adding to the excellent foundation of Deep Learning, this paper, proposes a novel CoWarriorNet model for rapid detection of CoVID-19, via chest X-ray images, which adds in an extra layer of precision and confirmation in the detection of cases in both pre-Covid and post-Covid conditions. The proposed classification model curves out an excellent accuracy of 97.8%, with the major eye-candy being the sensitivity rate of 0.99 when detecting CoVID-19 cases. This model introduces a new concept of Alpha Trimmed Average Pooling, which along with the novel architecture adds a subtle touch to its high efficiency, thereby giving a much-needed solution to the medical experts. The two-mouthed architecture provides the added benefit of a confidence score, deducing human aid in case of discrepancy. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00354-021-00143-1.
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Affiliation(s)
- Indrani Roy
- Department of Electronics and Communication Engineering, Calcutta Institute of Engineering and Management, Kolkata, 700040 WB India
| | - Rinita Shai
- Department of Mathematics, Behala College, Calcutta University, Kolkata, 700060 WB India
| | - Arijit Ghosh
- Department of Electronics and Communication Engineering, Calcutta Institute of Engineering and Management, Kolkata, 700040 WB India
| | - Anirban Bej
- Department of Electronics and Communication Engineering, Calcutta Institute of Engineering and Management, Kolkata, 700040 WB India
| | - Soumen Kumar Pati
- Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Haringhata, 741249 WB India
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Abstract
Diabetes is a disease that occurs when the body presents an uncontrolled level of glucose that is capable of damaging the retina, leading to permanent damage of the eyes or vision loss. When diabetes affects the eyes, it is known as diabetic retinopathy, which became a global medical problem among elderly people. The fundus oculi technique involves observing the eyeball to diagnose or check the pathology evolution. In this work, we implement a convolutional neural network model to process a fundus oculi image to recognize the eyeball structure and determine the presence of diabetic retinopathy. The model’s parameters are optimized using the transfer-learning methodology for mapping an image with the corresponding label. The model training and testing are performed with a dataset of medical fundus oculi images and a pathology severity scale present in the eyeball as labels. The severity scale separates the images into five classes, from a healthy eyeball to a proliferative diabetic retinopathy presence. The latter is probably a blind patient. Our proposal presented an accuracy of 97.78%, allowing for the confident prediction of diabetic retinopathy in fundus oculi images.
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