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Soni S, Seal A, Mohanty SK, Sakurai K. Electroencephalography signals-based sparse networks integration using a fuzzy ensemble technique for depression detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Qiu S, Ma J, Ma Z. IRCM-Caps: An X-ray image detection method for COVID-19. THE CLINICAL RESPIRATORY JOURNAL 2023; 17:364-373. [PMID: 36922395 DOI: 10.1111/crj.13599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/12/2023] [Accepted: 02/20/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE COVID-19 is ravaging the world, but traditional reverse transcription-polymerase reaction (RT-PCR) tests are time-consuming and have a high false-negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID-19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. METHODS The proposed model integrates CNN and CapsNet. And attention mechanism module and multi-branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. RESULT The test dataset includes 1200 X-ray images (400 COVID-19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception-Resnet-v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. CONCLUSION The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID-19 X-ray image dataset.
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Affiliation(s)
- Shuo Qiu
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Jinlin Ma
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China.,Key Laboratory of Intelligent Information Processing of Image and Graphics, State Ethnic Affairs Commission, Yinchuan, China
| | - Ziping Ma
- School of Mathematics and Information Science, North Minzu University, Yinchuan, China
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Aldhahi W, Sull S. Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability. Diagnostics (Basel) 2023; 13:441. [PMID: 36766546 PMCID: PMC9914375 DOI: 10.3390/diagnostics13030441] [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: 11/23/2022] [Revised: 01/08/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on patients and healthcare systems across the world. Distinguishing non-COVID-19 patients from COVID-19 patients at the lowest possible cost and in the earliest stages of the disease is a major issue. Additionally, the implementation of explainable deep learning decisions is another issue, especially in critical fields such as medicine. The study presents a method to train deep learning models and apply an uncertainty-based ensemble voting policy to achieve 99% accuracy in classifying COVID-19 chest X-rays from normal and pneumonia-related infections. We further present a training scheme that integrates the cyclic cosine annealing approach with cross-validation and uncertainty quantification that is measured using prediction interval coverage probability (PICP) as final ensemble voting weights. We also propose the Uncertain-CAM technique, which improves deep learning explainability and provides a more reliable COVID-19 classification system. We introduce a new image processing technique to measure the explainability based on ground-truth, and we compared it with the widely adopted Grad-CAM method.
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Affiliation(s)
| | - Sanghoon Sull
- School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
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Balan E, Saraniya O. Novel neural network architecture using sharpened cosine similarity for robust classification of Covid-19, pneumonia and tuberculosis diseases from X-rays. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
COVID-19 is a rapidly proliferating transmissible virus that substantially impacts the world population. Consequently, there is an increasing demand for fast testing, diagnosis, and treatment. However, there is a growing need for quick testing, diagnosis, and treatment. In order to treat infected individuals, stop the spread of the disease, and cure severe pneumonia, early covid-19 detection is crucial. Along with covid-19, various pneumonia etiologies, including tuberculosis, provide additional difficulties for the medical system. In this study, covid-19, pneumonia, tuberculosis, and other specific diseases are categorized using Sharpened Cosine Similarity Network (SCS-Net) rather than dot products in neural networks. In order to benchmark the SCS-Net, the model’s performance is evaluated on binary class (covid-19 and normal), and four-class (tuberculosis, covid-19, pneumonia, and normal) based X-ray images. The proposed SCS-Net for distinguishing various lung disorders has been successfully validated. In multiclass classification, the proposed SCS-Net succeeded with an accuracy of 94.05% and a Cohen’s kappa score of 90.70% ; in binary class, it achieved an accuracy of 96.67% and its Cohen’s kappa score of 93.70%. According to our investigation, SCS in deep neural networks significantly lowers the test error with lower divergence. SCS significantly increases classification accuracy in neural networks and speeds up training.
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Affiliation(s)
- Elakkiya Balan
- Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Chennai, Tamil Nadu, India
| | - O. Saraniya
- Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India
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Chakraverty S, Gupta D. As a pandemic strikes: A study on the impact of mental stress, emotion drifts and activities on community emotional well-being. MEASUREMENT : JOURNAL OF THE INTERNATIONAL MEASUREMENT CONFEDERATION 2022; 204:112121. [PMID: 36311377 PMCID: PMC9597569 DOI: 10.1016/j.measurement.2022.112121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 09/09/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
The widespread, ongoing COVID-19 pandemic has brought to the fore concerns regarding the psychological well-being of people. Recent research revealed various issues impacting mental health of people. However, a systematic study of the emotional drift of the populace, has been precluded so far. Our investigative research seeks to explore stress factors for different subgroups in India, variation in primary emotions during COVID-19 initial phase, and the emotional impact of activities practiced by people to adjust to the new norms. We conduct an online questionnaire-based survey that elicits responses from 958 participants. Our analysis establishes significant correlations between pandemic-induced causative factors and stresses in subgroups and micro-community. Unexpected events during the pandemic disturbed community's emotional equilibrium. Lastly, we find specific activities demonstrating an ameliorative impact on the emotional well-being of people. Our analysis emphasizes the need for a pre-planned infrastructure to provide Psychological First Aid (PFA) to foster psychological preparedness.
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Affiliation(s)
- Shampa Chakraverty
- Department of Computer Science and Engineering, Netaji Subhas University of Technology, Delhi, India
| | - Divya Gupta
- Department of Computer Science and Engineering, Netaji Subhas University of Technology, Delhi, India
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Banerjee A, Sarkar A, Roy S, Singh PK, Sarkar R. COVID-19 chest X-ray detection through blending ensemble of CNN snapshots. Biomed Signal Process Control 2022; 78:104000. [PMID: 35855489 PMCID: PMC9283670 DOI: 10.1016/j.bspc.2022.104000] [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: 03/30/2022] [Revised: 06/23/2022] [Accepted: 07/11/2022] [Indexed: 12/04/2022]
Abstract
The novel COVID-19 pandemic, has effectively turned out to be one of the deadliest events in modern history, with unprecedented loss of human life, major economic and financial setbacks and has set the entire world back quite a few decades. However, detection of the COVID-19 virus has become increasingly difficult due to the mutating nature of the virus, and the rise in asymptomatic cases. To counteract this and contribute to the research efforts for a more accurate screening of COVID-19, we have planned this work. Here, we have proposed an ensemble methodology for deep learning models to solve the task of COVID-19 detection from chest X-rays (CXRs) to assist Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of transfer learning for Convolutional Neural Networks (CNNs), widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The DenseNet-201 architecture has been trained only once to generate multiple snapshots, offering diverse information about the extracted features from CXRs. We follow the strategy of decision-level fusion to combine the decision scores using the blending algorithm through a Random Forest (RF) meta-learner. Experimental results confirm the efficacy of the proposed ensemble method, as shown through impressive results upon two open access COVID-19 CXR datasets - the largest COVID-X dataset, as well as a smaller scale dataset. On the large COVID-X dataset, the proposed model has achieved an accuracy score of 94.55% and on the smaller dataset by Chowdhury et al., the proposed model has achieved a 98.13% accuracy score.
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Affiliation(s)
- Avinandan Banerjee
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India
| | - Arya Sarkar
- Department of Computer Science, University of Engineering and Management, University Area, Plot No. III - B/5, New Town, Action Area - III, Kolkata 700160, West Bengal, India
| | - Sayantan Roy
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, West Bengal, India
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Bhattacharya D, Sharma D, Kim W, Ijaz MF, Singh PK. Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring. BIOSENSORS 2022; 12:bios12060393. [PMID: 35735541 PMCID: PMC9221472 DOI: 10.3390/bios12060393] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/28/2022] [Accepted: 06/05/2022] [Indexed: 06/12/2023]
Abstract
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selecting appropriate medications. Human Activity Recognition, abbreviated as 'HAR', is the prediction of common human measurements, which consist of movements such as walking, running, drinking, cooking, etc. It is extremely advantageous for services in the sphere of medical care, such as fitness trackers, senior care, and archiving patient information for future use. The two types of data that can be fed to the HAR system as input are, first, video sequences or images of human activities, and second, time-series data of physical movements during different activities recorded through sensors such as accelerometers, gyroscopes, etc., that are present in smart gadgets. In this paper, we have decided to work with time-series kind of data as the input. Here, we propose an ensemble of four deep learning-based classification models, namely, 'CNN-net', 'CNNLSTM-net', 'ConvLSTM-net', and 'StackedLSTM-net', which is termed as 'Ensem-HAR'. Each of the classification models used in the ensemble is based on a typical 1D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network; however, they differ in terms of their architectural variations. Prediction through the proposed Ensem-HAR is carried out by stacking predictions from each of the four mentioned classification models, then training a Blender or Meta-learner on the stacked prediction, which provides the final prediction on test data. Our proposed model was evaluated over three benchmark datasets, WISDM, PAMAP2, and UCI-HAR; the proposed Ensem-HAR model for biomedical measurement achieved 98.70%, 97.45%, and 95.05% accuracy, respectively, on the mentioned datasets. The results from the experiments reveal that the suggested model performs better than the other multiple generated measurements to which it was compared.
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Affiliation(s)
- Debarshi Bhattacharya
- Department of Electronics and Communication Engineering, Techno Main Salt Lake, Salt Lake City, EM-4/1, Sector-V, Kolkata 700091, West Bengal, India;
| | - Deepak Sharma
- Department of Information Technology, Jadavpur University Second Campus, Jadavpur University, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India; (D.S.); (P.K.S.)
| | - Wonjoon Kim
- Division of Future Convergence (HCI Science Major), Dongduk Women’s University, Seoul 02748, Korea
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University Second Campus, Jadavpur University, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata 700106, West Bengal, India; (D.S.); (P.K.S.)
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Mahanty C, Kumar R, Patro SGK. Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models. NEW GENERATION COMPUTING 2022; 40:1125-1141. [PMID: 35730008 PMCID: PMC9202670 DOI: 10.1007/s00354-022-00176-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/24/2022] [Indexed: 05/12/2023]
Abstract
One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders.
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Affiliation(s)
| | - Raghvendra Kumar
- Department of Computer Science and Engineering, GIET University, Gunupur, India
| | - S. Gopal Krishna Patro
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh India
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Detection and Prevention of Virus Infection. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1368:21-52. [DOI: 10.1007/978-981-16-8969-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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