CLASSIFIED COVID-19 BY DENSENET121-BASED DEEP TRANSFER LEARNING FROM CT-SCAN IMAGES
Article Sidebar
Main Article Content
Abstract
The COVID-19 disease, which has recently emerged and has been considered a worldwide pandemic, has had a significant impact on the lives of millions of people and has forced a substantial load on healthcare organizations. Numerous deep-learning models have been utilized for diagnosing coronaviruses from chest computed tomography (CT) images. However, in light of the limited availability of datasets on COVID-19, the pre-trained deep learning networks were used. The main objective of this research is to construct and develop an automated approach for the early detection and diagnosis of COVID-19 in thoracic CT images. This paper proposes the DDTL-COV model, a deep transfer learning model based on DenseNet121, to classify patients on CT scans as either COVID or non-COVID, utilizing weights obtained from the ImageNet dataset. Two datasets were used to train the DDTL-COV model: the SARS-CoV-2 CT-scan dataset and the COVID19-CT dataset. In the SARS-CoV-2 CT dataset, the model achieved a good accuracy of 99.6%. However, on the second dataset (COVID19-CT dataset), its performance shows an accuracy rate of 89%. These results show that the model performed better than alternative methods.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online.
How to Cite
References
Albelwi, Saleh A. 2022. “Deep Architecture Based on DenseNet-121 Model for Weather Image Recognition.” International Journal of Advanced Computer Science and Applications 13(10): 559–65.
De Anda-Suarez, Juan et al. 2022. “A Novel Metaheuristic Framework Based on the Generalized Boltzmann Distribution for COVID-19 Spread Characterization.” IEEE Access 10: 7326–40.
Biswas, Shreya et al. 2021. “Prediction of Covid-19 from Chest Ct Images Using an Ensemble of Deep Learning Models.” Applied Sciences (Switzerland) 11(15).
Cantarero, Ruben et al. 2021. “COVID19-Routes: A Safe Pedestrian Navigation Service.” IEEE Access 9: 93433–49.
Chen, Xiaocong et al. 2021. “Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images.” Pattern Recognition 113: 107826. https://doi.org/10.1016/j.patcog.2021.107826.
Dai, Hui et al. 2020. “High-Resolution Chest CT Features and Clinical Characteristics of Patients Infected with COVID-19 in Jiangsu, China.” International Journal of Infectious Diseases 95: 106–12. https://doi.org/10.1016/j.ijid.2020.04.003.
Dong, Di et al. 2021. “The Role of Imaging in the Detection and Management of COVID-19: A Review.” IEEE Reviews in Biomedical Engineering 14: 16–29.
Fei-Fei, L., J. Deng, and K. Li. 2010. “ImageNet: Constructing a Large-Scale Image Database.” Journal of Vision 9(8): 1037–1037.
Garain, Avishek et al. 2021. “Detection of COVID-19 from CT Scan Images: A Spiking Neural Network-Based Approach.” Neural Computing and Applications 1. https://doi.org/10.1007/s00521-021-05910-1.
Hasan, Najmul, Yukun Bao, Ashadullah Shawon, and Yanmei Huang. 2021. “DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.” SN Computer Science 2(5): 1–11. https://doi.org/10.1007/s42979-021-00782-7.
He, Xuehai et al. 2020. “Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans.” IEEE Transactions on Medical Imaging XX(Xx): 10.
Horry, Michael J. et al. 2020. “COVID-19 Detection through Transfer Learning Using Multimodal Imaging Data.” IEEE Access 8: 149808–24.
Huang, Mei Ling, and Yu Chieh Liao. 2022. “A Lightweight CNN-Based Network on COVID-19 Detection Using X-Ray and CT Images.” Computers in Biology and Medicine 146(March): 105604. https://doi.org/10.1016/j.compbiomed.2022.105604.
Hussain, Emtiaz et al. 2021. “CoroDet: A Deep Learning Based Classification for COVID-19 Detection Using Chest X-Ray Images.” Chaos, Solitons and Fractals 142: 110495. https://doi.org/10.1016/j.chaos.2020.110495.
Ibrahim, Walat Ramadhan, and Mayyadah Ramiz Mahmood. 2023. “COVID-19 Detection Based on Convolution Neural Networks from CT-Scan Images : A Review.” 29(3): 1668–77.
Islam, Md Rakibul, and Abdul Matin. 2020. “Detection of COVID 19 from CT Image by the Novel LeNet-5 CNN Architecture.” ICCIT 2020 - 23rd International Conference on Computer and Information Technology, Proceedings: 19–21.
Jaiswal, Aayush et al. 2021. “Classification of the COVID-19 Infected Patients Using DenseNet201 Based Deep Transfer Learning.” Journal of Biomolecular Structure and Dynamics 39(15): 5682–89. https://doi.org/10.1080/07391102.2020.1788642.
Kathamuthu, Nirmala Devi et al. 2023. “A Deep Transfer Learning-Based Convolution Neural Network Model for COVID-19 Detection Using Computed Tomography Scan Images for Medical Applications.” Advances in Engineering Software 175(October 2022): 103317. https://doi.org/10.1016/j.advengsoft.2022.103317.
Mohammed, Mazin Abed et al. 2020. “Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods.” IEEE Access 8: 99115–31.
Mondal, Arnab Kumar, Arnab Bhattacharjee, Parag Singla, and A. P. Prathosh. 2022. “XViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography.” IEEE Journal of Translational Engineering in Health and Medicine 10(November 2021): 1–10.
Munusamy, Hemalatha et al. 2021. “FractalCovNet Architecture for COVID-19 Chest X-Ray Image Classification and CT-Scan Image Segmentation.” Biocybernetics and Biomedical Engineering 41(3): 1025–38. https://doi.org/10.1016/j.bbe.2021.06.011.
Panwar, Harsh et al. 2020. “A Deep Learning and Grad-CAM Based Color Visualization Approach for Fast Detection of COVID-19 Cases Using Chest X-Ray and CT-Scan Images.” Chaos, Solitons and Fractals 140: 110190. https://doi.org/10.1016/j.chaos.2020.110190.
Pham, Tuan D. 2020. “A Comprehensive Study on Classification of COVID-19 on Computed Tomography with Pretrained Convolutional Neural Networks.” Scientific Reports 10(1): 1–8. https://doi.org/10.1038/s41598-020-74164-z.
Rahman, Tawsifur, Amith Khandakar, and Senior Member. 2021. “Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters.” IEEE Access 9: 120422–41.
Ramzy, Mohamed, Ibrahim Sherin, and M Youssef Karma. 2021. “Abnormality Detection and Intelligent Severity Assessment of Human Chest Computed Tomography Scans Using Deep Learning : A Case Study on SARS ‑ COV ‑ 2 Assessment.” Journal of Ambient Intelligence and Humanized Computing (0123456789). https://doi.org/10.1007/s12652-021-03282-x.
Sakib, Sadman et al. 2020. “DL-CRC: Deep Learning-Based Chest Radiograph Classification for Covid-19 Detection: A Novel Approach.” IEEE Access 8(July): 171575–89.
Sarker, Laboni et al. 2020. “COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images.” Preprints (May). https://github.com/mmiemon/COVID-DenseNet%0Ahttp://proxy.library.stonybrook.edu/login?url=https://search.proquest.com/docview/2414040424?accountid=14172%0Ahttps://www.preprints.org/manuscript/202005.0151/v1/download%0Ahttp://linksource.ebsco.com/linking.a.
Seum, Ashek, Amir Hossain Raj, Shadman Sakib, and Tonmoy Hossain. 2020. “A Comparative Study of CNN Transfer Learning Classification Algorithms with Segmentation for COVID-19 Detection from CT Scan Images.” Proceedings of 2020 11th International Conference on Electrical and Computer Engineering, ICECE 2020: 234–37.
Shah, Vruddhi et al. 2021. “Diagnosis of COVID-19 Using CT Scan Images and Deep Learning Techniques.” Emergency Radiology 28(3): 497–505.
Shi, Feng et al. 2021. “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19.” IEEE Reviews in Biomedical Engineering 14: 4–15.
Shukla, Prashant Kumar et al. 2020. “Efficient Prediction of Drug–Drug Interaction Using Deep Learning Models.” IET Systems Biology 14(4): 211–16.
Silva, Pedro et al. 2020. “COVID-19 Detection in CT Images with Deep Learning: A Voting-Based Scheme and Cross-Datasets Analysis.” Informatics in Medicine Unlocked 20: 100427. https://doi.org/10.1016/j.imu.2020.100427.
Soares, Eduardo, and Plamen Angelov. 2020. “A Large Dataset of Real Patients CT Scans for COVID-19 Identification.” Harv. Dataverse 1: 1–8.
Tabik, S. et al. 2020. “COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images.” IEEE Journal of Biomedical and Health Informatics 24(12): 3595–3605.
Tai, Yonghang et al. 2021. “Intelligent Intraoperative Haptic-AR Navigation for COVID-19 Lung Biopsy Using Deep Hybrid Model.” IEEE Transactions on Industrial Informatics 17(9): 6519–27.
Tang, Shengnan, Shouqi Yuan, and Yong Zhu. 2020. “Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis towards Rotating Machinery.” IEEE Access 8: 149487–96.
Wang, Zhao, Quande Liu, and Qi Dou. 2020. “Contrastive Cross-Site Learning with Redesigned Net for COVID-19 CT Classification.” IEEE Journal of Biomedical and Health Informatics 24(10): 2806–13.
Wu, Yu Huan et al. 2021. “JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.” IEEE Transactions on Image Processing 30: 3113–26.
Yan, Qingsen et al. 2021. “COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations.” IEEE Transactions on Big Data 7(1): 13–24.
Yu, Yongbin et al. 2020. “RMAF: Relu-Memristor-Like Activation Function for Deep Learning.” IEEE Access 8: 72727–41.