A Deep Learning Interpretable Model for Novel Coronavirus Disease (COVID-19) Screening with Chest CT Images

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Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
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"A Deep Learning Interpretable Model for Novel Coronavirus Disease (COVID-19) Screening with Chest CT Images"
written by Eri Matsuyama,
published by Journal of Biomedical Science and Engineering, Vol.13 No.7, 2020

has been cited by the following article(s):

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CrossRef

[1]	LitefusionNet: Boosting the Performance for Medical Image Classification with an Intelligent and Lightweight Feature Fusion Network
	Sabri, M Abdullah - Journal of Computational Science, 2024

[2]	Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms
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[3]	A Comprehensive Review on COVID-19 detection based on Cough Sounds, Symptoms, CXR and CT Images
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[4]	Comparative Analysis of Deepfake Image Detection Method Using VGG16, VGG19 and ResNet50
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[5]	MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis
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[6]	Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images
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[7]	Investigation of ConViT on COVID-19 Lung Image Classification and the Effects of Image Resolution and Number of Attention Heads
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[8]	An Integrated Approach to A Next-Generation Telemedicine and Health Advice System (ANGTHAS) with Artificial Intelligence
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[9]	Análisis de redes neuronales convolucionales para Eye-Tracking
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[10]	Real-time Facial Emotion Recognition Using FER2013 Image Dataset
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[11]	Application of Interpretable Artificial Intelligence Enabled Cognitive Internet of Things for COVID-19 Pandemics
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[12]	EDNC: Ensemble Deep Neural Network for COVID-19 Recognition. 2022, 8, x
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[13]	Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks
	Computers in biology …, 2022

[14]	EDNC: ensemble deep neural network for Covid-19 recognition
	Tomography, 2022

[15]	Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review
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[16]	COVID-19 Prediction, Diagnosis and Prevention Through Computer Vision
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[17]	A Comparative Analysis for Trash image classification using Deep Learning
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[18]	On the benchmarking of ResNet forgery image model using different datasets
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[19]	Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data
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[20]	Interpretable machine learning for covid-19: an empirical study on severity prediction task
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[21]	Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan
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[22]	ROI extraction in CT lung images of COVID-19 using Fast Fuzzy C means clustering
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[23]	COVID‐19 Diagnosis on CT Scan Images Using a Generative Adversarial Network and Concatenated Feature Pyramid Network with an Attention Mechanism
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[24]	A Summary of the Homogeneous 5D Universe Creation Model: Expressed in the Dirac Second-Order Quantization Representation
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[25]	Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies
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[26]	Covid-19 Recognition by Chest CT and Deep Learning
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[27]	Covid-19 Detection Using Chest Computed Tomography Scans on Ecuadorian Patients Who Live in the Highland Region
	2021 8th International …, 2021

[28]	Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning
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[29]	Do not repeat these mistakes--a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection
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[30]	Is AI Model Interpretable to Combat with COVID? An Empirical Study on Severity Prediction Task
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[31]	Vision Transformers for Covid-19 Cxr Image Severity Classification with Explainable Ai

[1]	Optimized deep learning model for comprehensive medical image analysis across multiple modalities Neurocomputing, 2025 DOI:10.1016/j.neucom.2024.129182

[2]	A Comprehensive Review on COVID-19 Detection Based on Cough Sounds, Symptoms, CXR, and CT Images IEEE Access, 2024 DOI:10.1109/ACCESS.2024.3396728

[3]	LitefusionNet: Boosting the performance for medical image classification with an intelligent and lightweight feature fusion network Journal of Computational Science, 2024 DOI:10.1016/j.jocs.2024.102324

[4]	Proceedings of 3rd International Conference on Smart Computing and Cyber Security Lecture Notes in Networks and Systems, 2024 DOI:10.1007/978-981-97-0573-3_2

[5]	Review of disease identification methods based on computed tomography imagery Ukrainian Journal of Information Technology, 2024 DOI:10.23939/ujit2024.01.095

[6]	A Comprehensive Review on COVID-19 Detection Based on Cough Sounds, Symptoms, CXR, and CT Images IEEE Access, 2024 DOI:10.1109/ACCESS.2024.3396728

[7]	Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms Multimedia Tools and Applications, 2024 DOI:10.1007/s11042-023-15485-9

[8]	Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task IEEE Transactions on Artificial Intelligence, 2023 DOI:10.1109/TAI.2021.3092698

[9]	Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images Moscow University Physics Bulletin, 2023 DOI:10.3103/S0027134923070275

[10]	Multiresolution analysis for COVID-19 diagnosis from chest CT images: wavelet vs. contourlet transforms Multimedia Tools and Applications, 2023 DOI:10.1007/s11042-023-15485-9

[11]	Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning Applied Soft Computing, 2023 DOI:10.1016/j.asoc.2023.110511

[12]	Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task IEEE Transactions on Artificial Intelligence, 2023 DOI:10.1109/TAI.2021.3092698

[13]	Explainable Machine Learning for Multimedia Based Healthcare Applications 2023 DOI:10.1007/978-3-031-38036-5_11

[14]	Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning Applied Soft Computing, 2023 DOI:10.1016/j.asoc.2023.110511

[15]	Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning Applied Soft Computing, 2023 DOI:10.1016/j.asoc.2023.110511

[16]	EDNC: Ensemble Deep Neural Network for COVID-19 Recognition Tomography, 2022 DOI:10.3390/tomography8020071

[17]	EDNC: Ensemble Deep Neural Network for COVID-19 Recognition Tomography, 2022 DOI:10.3390/tomography8020071

[18]	Prognostic Models in Healthcare: AI and Statistical Approaches Studies in Big Data, 2022 DOI:10.1007/978-981-19-2057-8_4

[19]	On the benchmarking of ResNet forgery image model using different datasets 2022 Human-Centered Cognitive Systems (HCCS), 2022 DOI:10.1109/HCCS55241.2022.10090275

[20]	On the benchmarking of ResNet forgery image model using different datasets 2022 Human-Centered Cognitive Systems (HCCS), 2022 DOI:10.1109/HCCS55241.2022.10090275

[21]	Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks Computers in Biology and Medicine, 2022 DOI:10.1016/j.compbiomed.2021.105123

[22]	Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review SN Computer Science, 2022 DOI:10.1007/s42979-022-01326-3

[23]	Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks Computers in Biology and Medicine, 2022 DOI:10.1016/j.compbiomed.2021.105123

[24]	Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan Computers in Biology and Medicine, 2021 DOI:10.1016/j.compbiomed.2021.104575

[25]	Covid-19 Detection Using Chest Computed Tomography Scans on Ecuadorian Patients Who Live in the Highland Region 2021 8th International Conference on Biomedical and Bioinformatics Engineering, 2021 DOI:10.1145/3502871.3502873

[26]	Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies Pattern Recognition, 2021 DOI:10.1016/j.patcog.2021.108035

[27]	Biomedical Engineering Tools for Management for Patients with COVID-19 2021 DOI:10.1016/B978-0-12-824473-9.00001-X

[28]	Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan Computers in Biology and Medicine, 2021 DOI:10.1016/j.compbiomed.2021.104575

[29]	COVID‐19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism Medical Physics, 2021 DOI:10.1002/mp.15044

[30]	Covid-19 Detection Using Chest Computed Tomography Scans on Ecuadorian Patients Who Live in the Highland Region 2021 8th International Conference on Biomedical and Bioinformatics Engineering, 2021 DOI:10.1145/3502871.3502873

[31]	Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan Computers in Biology and Medicine, 2021 DOI:10.1016/j.compbiomed.2021.104575

[32]	Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data 17th International Symposium on Medical Information Processing and Analysis, 2021 DOI:10.1117/12.2604743

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