A Novel Optimized Artificial Intelligence Based Deep Learning for Predicting the Infectious Disease Using Computed Tomography
Keywords:
COVID-19, Lungs, Computer Tomography, Infected Region, Tracking, Vulture Optimization, Convolutional Neural NetworkAbstract
The coronavirus disease from 2019 (COVID-19) spread over the world in 2020 and caused several health problems. Additionally, because it frequently affects the lungs, automatic detection is particularly crucial for protecting people from death. Using Computed Tomography (CT) images, the Artificial Vulture-based Anamorphic Depth Convolutional (AVbADC) Model is suggested in this study to segment the COVID-19 lungs affected region and categorize COVID-19 cases. Using CT scans of the lungs, the Modified AVbADC model separates COVID-19 infection from other pneumonia cases and normal pneumonia. The suggested architecture is built utilizing two parallel levels with various kernel sizes to capture the local and global properties of the inputs. It is based on the convolutional neural network. The outcomes of the experiment show that our AVbADC. On a short dataset, these results show a promising segmentation and classification performance; more improvements can be made with more training data. All things considered, the updated AVbADC model may be a useful tool for radiologists to aid in the diagnosis and early identification of COVID-19 cases. Finally, the proposed framework's results are contrasted with those of other methods currently in use in terms of sensitivity, accuracy, specificity, F-measure, and other factors.
Downloads
References
Chakraborty, Indranil, and Prasenjit Maity. "COVID-19 outbreak: Migration, effects on society, global environment and prevention." Science of the Total Environment 728 (2020): 138882.
Morís, Daniel Iglesias, et al. "Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable lungs X-ray images." Expert Systems with Applications 185 (2021): 115681.
Oulefki, Adel, et al. "Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images." Pattern recognition 114 (2021): 107747.
Tolkachev, Alexey, et al. "Deep learning for diagnosis and segmentation of pneumothorax: the results on the kaggle competition and validation against radiologists." IEEE Journal of Biomedical and Health Informatics 25.5 (2020): 1660-1672.
Feng, Zhichao, et al. "Machine learning based on clinical characteristics and lungs CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19." European radiology (2021): 1-11.
Bashir, Muhammad Farhan, M. A. Benjiang, and Luqman Shahzad. "A brief review of socio-economic and environmental impact of Covid-19." Air Quality, Atmosphere & Health 13.12 (2020): 1403-1409.
Tabary, Mohammadreza, et al. "Pathologic features of COVID-19: A concise review." Pathology-Research and Practice (2020): 153097.
Singh, Thakur Uttam, et al. "Drug repurposing approach to fight COVID-19." Pharmacological Reports (2020): 1-30.
Samudrala, Pavan Kumar, et al. "Virology, pathogenesis, diagnosis and in-line treatment of COVID-19." European Journal of Pharmacology 883 (2020): 173375.
Tian, Sufang, et al. "Pulmonary pathology of early-phase 2019 novel coronavirus (COVID-19) pneumonia in two patients with lung cancer." Journal of thoracic oncology 15.5 (2020): 700-704.
Arora, Amarpreet Singh, Himadri Rajput, and Rahil Changotra. "Current perspective of COVID-19 spread across South Korea: Exploratory data analysis and containment of the pandemic." Environment, development and sustainability 23.5 (2021): 6553-6563.
Lakshmanaprabu, S. K., et al. "Online clinical decision support system using optimal deep neural networks." Applied Soft Computing 81 (2019): 105487.
Abd El-Aziz, Tarek Mohamed, and James D. Stockand. "Recent progress and challenges in drug development against COVID-19 coronavirus (SARS-CoV-2)-an update on the status." Infection, Genetics and Evolution 83 (2020): 104327.
Kang, JaHyun, et al. "South Korea's responses to stop the COVID-19 pandemic." American Journal of Infection Control 48.9 (2020): 1080-1086.
Raghul, M., and K. Sharaj. "Deep Learning Technique for COVID 19 Prediction using CT Scan Images." 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2021.
Bhattacharya, Sweta, et al. "Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey." Sustainable cities and society 65 (2021): 102589.
Narin, Ali, Ceren Kaya, and Ziynet Pamuk. "Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks." Pattern Analysis and Applications (2021): 1-14.
Bini, Stefano A. "Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?." The Journal of arthroplasty 33.8 (2018): 2358-2361.
Ouchicha, Chaimae, Ouafae Ammor, and Mohammed Meknassi. "CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from lungs x-ray images." Chaos, Solitons & Fractals 140 (2020): 110245.
Shi, Feng, et al. "Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19." IEEE reviews in biomedical engineering 14 (2020): 4-15.
Fan, Deng-Ping, et al. "Inf-net: Automatic covid-19 lung infection segmentation from ct images." IEEE Transactions on Medical Imaging 39.8 (2020): 2626-2637.
Voulodimos, Athanasios, et al. "Deep learning models for COVID-19 infected area segmentation in CT images." The 14th PErvasive Technologies Related to Assistive Environments Conference. 2021.
Li, Zhang, et al. "From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans." European radiology 30.12 (2020): 6828-6837.
Kuchana, Maheshwar, et al. "AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Lungs CT scans." Multimedia tools and applications 80.6 (2021): 9161-9175.
Pu, Jiantao, et al. "Automated quantification of COVID-19 severity and progression using lungs CT images." European Radiology 31.1 (2021): 436-446.
Paluru, Naveen, et al. "Anam-Net: Anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 lungs CT images." IEEE Transactions on Neural Networks and Learning Systems 32.3 (2021): 932-946.
Ni, Qianqian, et al. "A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in lungs CT images." European radiology 30.12 (2020): 6517-6527.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
