Volume 16 , Issue 2 , PP: 42-59, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Md Jabed Hussain 1 * , Awakash Mishra 2
Doi: https://doi.org/10.54216/JISIoT.160204
For public health systems worldwide, the COVID-19 epidemic has presented hitherto unheard-of difficulties. Rapid and accurate virus detection is essential for successful treatment and containment. This paper explores the transformative potential of Artificial Intelligence (AI) and Big Data in public health, focusing on applying deep learning techniques for COVID-19 detection in medical imaging. We discuss the integration of AI-driven solutions in healthcare, the role of big data in enhancing diagnostic accuracy, and the implications for future public health strategies. The COVID-19 pandemic started in Dec 2019 and has wreaked havoc on our lives ever since. One such youngest addition to the coronavirus family has claimed the lives of almost half the world's population. With the introduction of constantly evolving forms of this infection, locating the infection early on would still be essential. Even though the PCR test is the best and most utilized approach for identification, non-contact procedures such as chest radiography and CT scans have always been recommended. In this context, artificial intelligence is integral to the early and precise diagnosis of COVID-19 via lung image processing. The primary aim of this study is to evaluate and contrast multiple deep learning improved strategies for detecting COVID-19 in CT and X-Ray medical images. We employed four strong CNN methods for the COVID-19 images of the binary classification challenge: ResNet152, VGG16, ResNet50, and DenseNet121. The suggested Attention-based ResNet framework is created to choose the appropriate architecture and training settings for models automatically. In the diagnosis of COVID-19 utilizing CT-scan images, the accuracy and F1-score are over 96 percent. In addition, transfer-learning methods were used to address the lack of information and shorten the training time. Enhanced VGG16 deep transfer learning design was used to accomplish multi-class categorization of X-ray imaging tasks. Enhanced VGG16 was shown to have 99 percent accuracy in detecting X-ray imaging from three classes: Normal, COVID-19, and Pneumonia. The algorithms' accuracy and validity were tested on well-known public datasets of X-ray and CT scans. For COVID-19 diagnosis, the presented approaches outperform previous methods in the literature. In the fight against COVID-19, we believe our research will aid virologists and radiologists in making better and faster diagnoses.
Public Health , AI , Big Data , Deep Learning , COVID-19 , Medical Imaging
[1] F. M. Shah et al., "A Comprehensive Survey of COVID-19 Detection Using Medical Images," SN Computer Science, vol. 2, 2021.
[2] M. Riaz, M. M. Bashir, and I. Younas, "Metaheuristics based COVID-19 detection using medical images: A review," Computers in Biology and Medicine, vol. 144, p. 105344, 2022.
[3] S. Liang et al., "Fast, automated detection of COVID-19 from medical images using convolutional neural networks," Communications Biology, vol. 4, 2021.
[4] M. Singh et al., "Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data," Medical & Biological Engineering & Computing, vol. 59, pp. 825-839, 2021.
[5] A. Das et al., "Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network," Pattern Analysis and Applications, vol. 24, pp. 1111-1124, 2021.
[6] M. J. Horry et al., "X-Ray Image-based COVID-19 Detection using Pre-trained Deep Learning Models," 2020.
[7] S. Bansal, M. Singh, R. K. Dubey, and B. K. Panigrahi, "Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data," Journal of Medical and Biological Engineering, pp. 1-12, 2021.
[8] R. Zhu, A. Boukerche, L. Long, and Q. Yang, "Design Guidelines on Trust Management for Underwater Wireless Sensor Networks," IEEE Communications Surveys & Tutorials, vol. 26, pp. 1-23, 2024.
[9] M. Murugappan et al., "Artificial Intelligence Based COVID-19 Detection using Medical Imaging Methods: A Review," Computational Modelling and Imaging for SARS-CoV-2 and COVID-19, 2021.
[10] T. Agrawal and P. Choudhary, "FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images," Evolving Systems, pp. 1-15, 2021.
[11] W. Zhang et al., "Dynamic-Fusion-Based Federated Learning for COVID-19 Detection," IEEE Internet of Things Journal, vol. 8, pp. 15884-15891, 2021.
[12] A. Abbas, M. M. Abdelsamea, and M. M. Gaber, "Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network," Applied Intelligence, pp. 1-11, 2021.
[13] B. Ghoshal and A. Tucker, "Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection," ArXiv, abs/2003.10769, 2020.
[14] M. Z. Islam, M. M. Islam, and A. Asraf, "A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images," medRxiv, 2020.
[15] S. A. Mahmoudi et al., "Explainable Deep Learning for COVID-19 Detection Using Chest X-ray and CT-Scan Images," Healthcare Informatics for Fighting COVID-19 and Future Epidemics, 2021.
[16] T. Zebin and S. Rezvy, "COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization," Applied Intelligence, vol. 51, pp. 1010-1021, 2021.
[17] M. Shorfuzzaman et al., "Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images," Journal of Healthcare Engineering, 2021.
[18] M. J. Horry et al., "COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data," IEEE Access, vol. 8, pp. 149808-149824, 2020.
[19] A. M. Alqudah, S. Qazan, and A. Alqudah, "Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks," 2020.
[20] N. M. Khalifa et al., "The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach," Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach, vol. 78, pp. 73-90, 2020.
[21] E. Jangam, A. A. Barreto, and C. S. Annavarapu, "Automatic detection of COVID-19 from chest CT scan and chest X-ray images using deep learning, transfer learning, and stacking," Applied Intelligence, vol. 52, pp. 2243-2259, 2022.
[22] T. Padma and C. U. Kumari, "Deep Learning Based Chest X-Ray Image as a Diagnostic Tool for COVID-19," 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 589-592, 2020.
[23] M. A. Nawshad et al., "Attention Based Residual Network for Effective Detection of COVID-19 and Viral Pneumonia," 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1-7, 2021.
[24] A. Ashraf, A. U. Malik, and Z. H. Khan, "POSTER: Diagnosis of COVID-19 through Transfer Learning Techniques on CT Scans: A Comparison of Deep Learning Models," ArXiv, abs/2203.09348, 2022.
[25] N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, "Automatic Diagnosis of COVID-19 Related Pneumonia from CXR and CT-Scan Images," Engineering, Technology & Applied Science Research, 2022.
[26] M. Y. Kamil, "A deep learning framework to detect COVID-19 disease via chest X-ray and CT scan images," International Journal of Electrical and Computer Engineering, vol. 11, pp. 844-850, 2021.
[27] I. Chouat et al., "COVID-19 detection in CT and CXR images using deep learning models," Biogerontology, pp. 1-20, 2022.
[28] S. Patel, "Classification of COVID-19 from chest X-ray images using a deep convolutional neural network," Turkish Journal of Computer and Mathematics Education, vol. 12, pp. 2643-2651, 2021.
[29] K. Neha, K. P. Joshi, N. A. Jyothi, and J. V. Kumar, "Preliminary Detection of COVID-19 Using Deep Learning and Machine Learning Techniques on Radiological Data," 2021.
[30] D. Olcer and Ç. B. Erdaş, "A DEEP LEARNING APPROACH FED BY CT SCANS FOR DIAGNOSIS OF COVID-19," 2020.