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Wang B, Qi J, An X, Wang Y. Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition. PLoS One 2024; 19:e0297655. [PMID: 38300934 PMCID: PMC10833553 DOI: 10.1371/journal.pone.0297655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024] Open
Abstract
Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.
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Affiliation(s)
- Buyu Wang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
| | - Jingwei Qi
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Xiaoping An
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
| | - Yuan Wang
- Key Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Agricultural University, Inner Mongolia, China
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China
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2
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Gopatoti A, Vijayalakshmi P. MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images. Biomed Signal Process Control 2023; 85:104857. [PMID: 36968651 PMCID: PMC10027978 DOI: 10.1016/j.bspc.2023.104857] [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: 10/05/2022] [Revised: 02/13/2023] [Accepted: 03/11/2023] [Indexed: 03/24/2023]
Abstract
Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the Whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet.
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Affiliation(s)
- Anandbabu Gopatoti
- Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India
- Centre for Research, Anna University, Chennai, Tamil Nadu, India
| | - P Vijayalakshmi
- Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India
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3
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Jaiswal P, Bhirud D. An intelligent deep network for dental medical image processing system. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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4
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Srivastava G, Chauhan A, Kargeti N, Pradhan N, Dhaka VS. ApneaNet: A hybrid 1DCNN-LSTM architecture for detection of Obstructive Sleep Apnea using digitized ECG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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5
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Riedel P, von Schwerin R, Schaudt D, Hafner A, Späte C. ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:203-224. [PMID: 37359194 PMCID: PMC10265567 DOI: 10.1007/s41666-023-00132-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/20/2023] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
Personal health data is subject to privacy regulations, making it challenging to apply centralized data-driven methods in healthcare, where personalized training data is frequently used. Federated Learning (FL) promises to provide a decentralized solution to this problem. In FL, siloed data is used for the model training to ensure data privacy. In this paper, we investigate the viability of the federated approach using the detection of COVID-19 pneumonia as a use case. 1411 individual chest radiographs, sourced from the public data repository COVIDx8 are used. The dataset contains radiographs of 753 normal lung findings and 658 COVID-19 related pneumonias. We partition the data unevenly across five separate data silos in order to reflect a typical FL scenario. For the binary image classification analysis of these radiographs, we propose ResNetFed, a pre-trained ResNet50 model modified for federation so that it supports Differential Privacy. In addition, we provide a customized FL strategy for the model training with COVID-19 radiographs. The experimental results show that ResNetFed clearly outperforms locally trained ResNet50 models. Due to the uneven distribution of the data in the silos, we observe that the locally trained ResNet50 models perform significantly worse than ResNetFed models (mean accuracies of 63% and 82.82%, respectively). In particular, ResNetFed shows excellent model performance in underpopulated data silos, achieving up to +34.9 percentage points higher accuracy compared to local ResNet50 models. Thus, with ResNetFed, we provide a federated solution that can assist the initial COVID-19 screening in medical centers in a privacy-preserving manner.
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Affiliation(s)
- Pascal Riedel
- Institute for Informatics, University of Applied Sciences, Prittwitzstraße 10, Ulm, 89075 Baden-Württemberg Germany
| | - Reinhold von Schwerin
- Institute for Informatics, University of Applied Sciences, Prittwitzstraße 10, Ulm, 89075 Baden-Württemberg Germany
| | - Daniel Schaudt
- Institute for Informatics, University of Applied Sciences, Prittwitzstraße 10, Ulm, 89075 Baden-Württemberg Germany
| | - Alexander Hafner
- Institute for Informatics, University of Applied Sciences, Prittwitzstraße 10, Ulm, 89075 Baden-Württemberg Germany
| | - Christian Späte
- Transferzentrum für Digitalisierung, Analytics & Data Science Ulm (DASU), Ensingerstraße 4, Ulm, 89073 Baden-Württemberg Germany
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6
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Chakraborty GS, Batra S, Singh A, Muhammad G, Torres VY, Mahajan M. A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics (Basel) 2023; 13:diagnostics13101806. [PMID: 37238290 DOI: 10.3390/diagnostics13101806] [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: 04/07/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient's life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.
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Affiliation(s)
- Gouri Shankar Chakraborty
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Salil Batra
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Aman Singh
- Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
- Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Vanessa Yelamos Torres
- Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Engineering Research & Innovation Group, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche C.P. 24560, Mexico
| | - Makul Mahajan
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
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7
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Sultana A, Nahiduzzaman M, Bakchy SC, Shahriar SM, Peyal HI, Chowdhury MEH, Khandakar A, Arselene Ayari M, Ahsan M, Haider J. A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094458. [PMID: 37177662 PMCID: PMC10181786 DOI: 10.3390/s23094458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/19/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023]
Abstract
Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.
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Affiliation(s)
- Abida Sultana
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Sagor Chandro Bakchy
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Saleh Mohammed Shahriar
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Hasibul Islam Peyal
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester Street, Manchester M1 5GD, UK
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8
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Rehman A, Khan A, Fatima G, Naz S, Razzak I. Review on chest pathogies detection systems using deep learning techniques. Artif Intell Rev 2023; 56:1-47. [PMID: 37362896 PMCID: PMC10027283 DOI: 10.1007/s10462-023-10457-9] [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] [Indexed: 03/29/2023]
Abstract
Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated. A tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations.
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Affiliation(s)
- Arshia Rehman
- COMSATS University Islamabad, Abbottabad-Campus, Abbottabad, Pakistan
| | - Ahmad Khan
- COMSATS University Islamabad, Abbottabad-Campus, Abbottabad, Pakistan
| | - Gohar Fatima
- The Islamia University of Bahawalpur, Bahawal Nagar Campus, Bahawal Nagar, Pakistan
| | - Saeeda Naz
- Govt Girls Post Graduate College No.1, Abbottabad, Pakistan
| | - Imran Razzak
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
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9
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Hu M, Wu X, Wang X, Xing Y, An N, Shi P. Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning. Biomed Signal Process Control 2023; 81:104487. [PMID: 36530216 PMCID: PMC9735266 DOI: 10.1016/j.bspc.2022.104487] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/13/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Blood Oxygen ( SpO 2 ), a key indicator of respiratory function, has received increasing attention during the COVID-19 pandemic. Clinical results show that patients with COVID-19 likely have distinct lower SpO 2 before the onset of significant symptoms. Aiming at the shortcomings of current methods for monitoring SpO 2 by face videos, this paper proposes a novel multi-model fusion method based on deep learning for SpO 2 estimation. The method includes the feature extraction network named Residuals and Coordinate Attention (RCA) and the multi-model fusion SpO 2 estimation module. The RCA network uses the residual block cascade and coordinate attention mechanism to focus on the correlation between feature channels and the location information of feature space. The multi-model fusion module includes the Color Channel Model (CCM) and the Network-Based Model(NBM). To fully use the color feature information in face videos, an image generator is constructed in the CCM to calculate SpO 2 by reconstructing the red and blue channel signals. Besides, to reduce the disturbance of other physiological signals, a novel two-part loss function is designed in the NBM. Given the complementarity of the features and models that CCM and NBM focus on, a Multi-Model Fusion Model(MMFM) is constructed. The experimental results on the PURE and VIPL-HR datasets show that three models meet the clinical requirement(the mean absolute error ⩽ 2%) and demonstrate that the multi-model fusion can fully exploit the SpO 2 features of face videos and improve the SpO 2 estimation performance. Our research achievements will facilitate applications in remote medicine and home health.
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Affiliation(s)
- Min Hu
- Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education,Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, Anhui 230601, China
| | - Xia Wu
- Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education,Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, Anhui 230601, China
| | - Xiaohua Wang
- Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education,Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, Anhui 230601, China
| | - Yan Xing
- School of Mathematics, Hefei University of Technology, Hefei, Anhui 230601, China
| | - Ning An
- Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education,Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, Anhui 230601, China
- National Smart Eldercare International S&T Cooperation Base, Hefei University of Technology, Hefei, Anhui 230601, China
| | - Piao Shi
- Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education,Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, Anhui 230601, China
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10
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Domain-ensemble learning with cross-domain mixup for thoracic disease classification in unseen domains. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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11
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Marefat A, Marefat M, Hassannataj Joloudari J, Nematollahi MA, Lashgari R. CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers. Front Public Health 2023; 11:1025746. [PMID: 36923036 PMCID: PMC10009152 DOI: 10.3389/fpubh.2023.1025746] [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: 10/15/2022] [Accepted: 02/07/2023] [Indexed: 03/03/2023] Open
Abstract
COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.
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Affiliation(s)
- Abdolreza Marefat
- Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mahdieh Marefat
- Department of Cellular and Molecular Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | | | - Reza Lashgari
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
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12
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Ahmed SM, Mstafa RJ. Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models. Diagnostics (Basel) 2022; 12:diagnostics12122939. [PMID: 36552945 PMCID: PMC9777157 DOI: 10.3390/diagnostics12122939] [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: 10/22/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022] Open
Abstract
Recently, many diseases have negatively impacted people's lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, and low-cost computer-based tools for the early prediction of knee OA patients are urgently needed. In this paper, as part of addressing this issue, we developed a new method to efficiently diagnose and classify knee osteoarthritis severity based on the X-ray images to classify knee OA in (i.e., binary and multiclass) in order to study the impact of different class-based, which has not yet been addressed in previous studies. This will provide physicians with a variety of deployment options in the future. Our proposed models are basically divided into two frameworks based on applying pre-trained convolutional neural networks (CNN) for feature extraction as well as fine-tuning the pre-trained CNN using the transfer learning (TL) method. In addition, a traditional machine learning (ML) classifier is used to exploit the enriched feature space to achieve better knee OA classification performance. In the first one, we developed five classes-based models using a proposed pre-trained CNN for feature extraction, principal component analysis (PCA) for dimensionality reduction, and support vector machine (SVM) for classification. While in the second framework, a few changes were made to the steps in the first framework, the concept of TL was used to fine-tune the proposed pre-trained CNN from the first framework to fit the two classes, three classes, and four classes-based models. The proposed models are evaluated on X-ray data, and their performance is compared with the existing state-of-the-art models. It is observed through conducted experimental analysis to demonstrate the efficacy of the proposed approach in improving the classification accuracy in both multiclass and binary class-based in the OA case study. Nonetheless, the empirical results revealed that the fewer multiclass labels used, the better performance achieved, with the binary class labels outperforming all, which reached a 90.8% accuracy rate. Furthermore, the proposed models demonstrated their contribution to early classification in the first stage of the disease to help reduce its progression and improve people's quality of life.
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Affiliation(s)
- Sozan Mohammed Ahmed
- Department of Computer Science, Faculty of Science, University of Zakho, Duhok 42002, Iraq
| | - Ramadhan J. Mstafa
- Department of Computer Science, Faculty of Science, University of Zakho, Duhok 42002, Iraq
- Department of Computer Science, College of Science, Nawroz University, Duhok 42001, Iraq
- Correspondence:
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13
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CJT-DEO: Condorcet’s Jury Theorem and Differential Evolution Optimization based ensemble of deep neural networks for pulmonary and colorectal cancer classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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Addo D, Zhou S, Jackson JK, Nneji GU, Monday HN, Sarpong K, Patamia RA, Ekong F, Owusu-Agyei CA. EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images. Diagnostics (Basel) 2022; 12:2569. [PMID: 36359413 PMCID: PMC9689048 DOI: 10.3390/diagnostics12112569] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/13/2022] [Accepted: 10/18/2022] [Indexed: 09/08/2024] Open
Abstract
The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.
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Affiliation(s)
- Daniel Addo
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Shijie Zhou
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Jehoiada Kofi Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Grace Ugochi Nneji
- Department of Computing, Oxford Brookes College of Chengdu University of Technology, Chengdu 610059, China
| | - Happy Nkanta Monday
- Department of Computing, Oxford Brookes College of Chengdu University of Technology, Chengdu 610059, China
| | - Kwabena Sarpong
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Rutherford Agbeshi Patamia
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Favour Ekong
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
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Srivastava G, Pradhan N, Saini Y. Ensemble of Deep Neural Networks based on Condorcet's Jury Theorem for screening Covid-19 and Pneumonia from radiograph images. Comput Biol Med 2022; 149:105979. [PMID: 36063689 PMCID: PMC9404085 DOI: 10.1016/j.compbiomed.2022.105979] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/03/2022] [Accepted: 08/13/2022] [Indexed: 11/04/2022]
Abstract
COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. Deep Neural Networks with hundreds or even millions of parameters (weights) are referred to as "black boxes" because their behavior is difficult to comprehend, even when the model's structure and weights are visible. On the same dataset, different Deep Convolutional Neural Networks perform differently. So, we do not necessarily have to rely on just one model; instead, we can evaluate our final score by combining multiple models. While including multiple models in the voter pool, it is not always true that the accuracy will improve. So, In this regard, the authors proposed a novel approach to determine the voting ensemble score of individual classifiers based on Condorcet's Jury Theorem (CJT). The authors demonstrated that the theorem holds while ensembling the N number of classifiers in Neural Networks. With the help of CJT, the authors proved that a model's presence in the voter pool would improve the likelihood that the majority vote will be accurate if it is more accurate than the other models. Besides this, the authors also proposed a Domain Extended Transfer Learning (DETL) ensemble model as a soft voting ensemble method and compared it with CJT based ensemble method. Furthermore, as deep learning models typically fail in real-world testing, a novel dataset has been used with no duplicate images. Duplicates in the dataset are quite problematic since they might affect the training process. Therefore, having a dataset devoid of duplicate images is considered to prevent data leakage problems that might impede the thorough assessment of the trained models. The authors also employed an algorithm for faster training to save computational efforts. Our proposed method and experimental results outperformed the state-of-the-art with the DETL-based ensemble model showing an accuracy of 97.26%, COVID-19, sensitivity of 98.37%, and specificity of 100%. CJT-based ensemble model showed an accuracy of 98.22%, COVID-19, sensitivity of 98.37%, and specificity of 99.79%.
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Affiliation(s)
- Gaurav Srivastava
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India.
| | - Nitesh Pradhan
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India.
| | - Yashwin Saini
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
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