Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification
Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-bas...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10787 |
| _version_ | 1797469265314447360 |
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| author | Soumadip Ghosh Suharta Banerjee Supantha Das Arnab Hazra Saurav Mallik Zhongming Zhao Ayan Mukherji |
| author_facet | Soumadip Ghosh Suharta Banerjee Supantha Das Arnab Hazra Saurav Mallik Zhongming Zhao Ayan Mukherji |
| author_sort | Soumadip Ghosh |
| collection | DOAJ |
| description | Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-based framework for the detection of COVID-19 status from chest X-ray and CT scan imaging data acquired from three benchmark imagery datasets. VGG-19, ResNet-50 and Inception-V3 models are employed in this research study to perform image classification. A variety of evaluation metrics including kappa statistic, Root-Mean-Square Error (RMSE), accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Recall, precision, and F-measure are used to ensure adequate performance of the proposed framework. Our findings indicate that the Inception-V3 model has the best performance in terms of COVID-19 status detection. |
| first_indexed | 2024-03-09T19:17:57Z |
| format | Article |
| id | doaj.art-421243998d774c4a85f9c06176dda410 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:57Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-421243998d774c4a85f9c06176dda4102023-11-24T03:32:49ZengMDPI AGApplied Sciences2076-34172022-10-0112211078710.3390/app122110787Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status ClassificationSoumadip Ghosh0Suharta Banerjee1Supantha Das2Arnab Hazra3Saurav Mallik4Zhongming Zhao5Ayan Mukherji6Department of Computer Science & Engineering, Institute of Engineering & Management, Kolkata 700091, WB, IndiaDepartment of Computer Science & Engineering, Meghnad Saha Institute of Technology, Kolkata 700150, WB, IndiaDepartment of Computer Science & Engineering, Academy of Technology, Aedconagar, Hooghly 712121, WB, IndiaDepartment of Computer Science & Engineering, Future Institute of Technology, Garia Boral Main Road, Kolkata 700154, WB, IndiaDepartment of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USACenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USADepartment of Computer Science & Engineering, Pailan College of Management & Technology, Kolkata 700104, WB, IndiaAccurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-based framework for the detection of COVID-19 status from chest X-ray and CT scan imaging data acquired from three benchmark imagery datasets. VGG-19, ResNet-50 and Inception-V3 models are employed in this research study to perform image classification. A variety of evaluation metrics including kappa statistic, Root-Mean-Square Error (RMSE), accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Recall, precision, and F-measure are used to ensure adequate performance of the proposed framework. Our findings indicate that the Inception-V3 model has the best performance in terms of COVID-19 status detection.https://www.mdpi.com/2076-3417/12/21/10787coronavirus disease 2019image classificationconvolutional neural networkInception-V3 |
| spellingShingle | Soumadip Ghosh Suharta Banerjee Supantha Das Arnab Hazra Saurav Mallik Zhongming Zhao Ayan Mukherji Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification Applied Sciences coronavirus disease 2019 image classification convolutional neural network Inception-V3 |
| title | Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification |
| title_full | Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification |
| title_fullStr | Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification |
| title_full_unstemmed | Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification |
| title_short | Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification |
| title_sort | evaluation and optimization of biomedical image based deep convolutional neural network model for covid 19 status classification |
| topic | coronavirus disease 2019 image classification convolutional neural network Inception-V3 |
| url | https://www.mdpi.com/2076-3417/12/21/10787 |
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