A Machine Learning Approach for Early Identification and Prevention of Covid-19 like Global Pandemics
Keywords:
Coronavirus, COVID-19 Pandemic, Automated Detection, Probabilistic Classification, Deep Neural Networks.Abstract
In addition to causing enormous global financial and social losses, the COVID-19 pandemic was a once-in-a-century occurrence that claimed a great deal of human lives. Reducing the number of victims can be achieved in part by precisely and early detection of the disease. The unanticipated surge in cases has also resulted in severe limits on the number of scans conducted and the amount of time radiologists may spend analyzing the data to assess the severity of the diseases and potential advancements in the future. Therefore, automated procedures that might lessen the burden on the healthcare system to offer prompt and accurate diagnoses are being researched. Furthermore, determining future possible hotspots for these diseases might aid in the selection of micro-containment zones, which can be a proactive measure to impede the disease's rapid spread. Numerous methods based on deep learning and machine learning have been researched for picture categorization, finding possible hotspots, or both. This work offers a data-driven approach to categories Covid-19 images and pinpoint possible pandemic hotspots, which will help with treatment and prevent pandemics from spreading in the future. Moreover, a deep neural network based regression model has been developed which predicts the number of new active cases in the days ahead. This approach is allows for early assessment of the disease spread and allows for timely management and early prevention of widespread pandemics. To assess the effectiveness of the suggested method, a comparison with the most recent iterations of deep learning and machine learning algorithms has been provided. Findings show that, when compared to baseline methods, the suggested data-driven strategy with probabilistic categorization performed better. Moreover, the proposed regression model outperforms baseline state of the art models in terms of accuracy of prediction..
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J. Miah, R. H. Khan, S. Ahmed and M. I. Mahmud, "A comparative study of Detecting Covid 19 by Using Chest X-ray Images– A Deep Learning Approach," 2023 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2023, pp. 0311-0316
A. Hussain, S. U. Amin, H. Lee, A. Khan, N. F. Khan and S. Seo, "An Automated Chest X-Ray Image Analysis for Covid-19 and Pneumonia Diagnosis Using Deep Ensemble Strategy," in IEEE Access, 2023, vol. 11, pp. 97207-97220,.
S. Mishra and A. Mantri, "Design of COVID19 Detection based on Relative Eccentric Feature Selection using Deep Vectorized Regressive Neural Network for Corona Virus," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 1-6.
J Somasekar, PP Kumar, A Sharma, “Machine learning and image analysis applications in the fight against COVID-19 pandemic: Datasets, research directions, challenges and opportunities”, Proceedings in Matrials Today, Elsevier 2021.
J. De Moura et al., "Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices," in IEEE Access, 2020, vol. 8, pp. 195594-195607
S Kumar, RD Raut, BE Narkhede,” A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers”, International Journal of Healthcare Management, Taylor and Francis, 2020, vol.13, no.4, pp. 337-345.
JB Awotunde, SO Folorunso, RG Jimoh, “Application of artificial intelligence for COVID-19 epidemic: an exploratory study, opportunities, challenges, and future prospects”, Artificial Intelligence for COVID-19. Studies in Systems, Decision and Control, Springer, 2021, vol 358.
S. Hu et al., "Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images," in IEEE Access, 2020, vol. 8, pp. 118869-118883.
J. C. Clement, V. Ponnusamy, K. C. Sriharipriya and R. Nandakumar, "A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis," in IEEE Reviews in Biomedical Engineering, 2022, vol. 15, pp. 325-340.
H. S. Alghamdi, G. Amoudi, S. Elhag, K. Saeedi and J. Nasser, "Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey," in IEEE Access, 2021, vol. 9, pp. 20235-20254.
T Ozturk, M Talo, EA Yildirim, UB Baloglu, “Automated detection of COVID-19 cases using deep neural networks with X-ray images”, Computers and Biology and Medicine, Elsevier 2020, col.121, 103792.
J. C. Clement, V. Ponnusamy, K. C. Sriharipriya and R. Nandakumar, "A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis," in IEEE Reviews in Biomedical Engineering, 2022, vol. 15, pp. 325-340
M. M. Islam, F. Karray, R. Alhajj and J. Zeng, "A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)," in IEEE Access, vol. 9, pp. 30551-30572.
Y Zhang, Q Zhang, Y Zhao, Y Deng, H Zheng, “Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic”, International Journal of Applied Earth Observation and Geoinformation, , Elsevier, 2022, vol. 112, 102942.
L Xu, R Magar, AB Farimani, “Forecasting COVID-19 new cases using deep learning methods”, Computers in biology and medicine, Elsevier 2022, vol.144, 105342
AS Kwekha-Rashid, HN Abduljabbar, B Alhayani, “Coronavirus disease (COVID-19) cases analysis using machine-learning applications”, Applied Nanoscience, Springer, 2023, vol.31, pp. 2013–2025.
A Mollalo, B Vahedi, S Bhattarai, LC Hopkins, “Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms”, International Journal of Medical Informatics, Elsevier, 2021, vol. 142, 104248.
SD Khan, L Alarabi, S Basalamah -, “Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network”, Journal of Computers, MDPI, 2020, vol.9, no.9, pp1-16.
A Akbarimajd, N Hoertel, MA Hussain, “Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images”, Journal of Computational Science, Elsevier 2022, vol.63, 101763.
M Momeny, AA Neshat, MA Hussain, S Kia, “Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images”, Computers in Biology and Medicine, Elsevier, 2022, vol.136, 104704.
MF Aslan, MF Unlersen, K Sabanci, A Durdu, “CNN-based transfer learning–BiLSTM
NN Das, N Kumar, M Kaur, V Kumar, D Singh, “Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays”, IBRM, Elsevier, 2022, vol.43, no.2, pp.114-119.
D. A. Zebari, A. M. Abdulazeez, D. Q. Zeebaree and M. S. Salih, "A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images," 2020 International Conference on Advanced Science and Engineering (ICOASE), 2020, pp. 1-6.
O. El Gannour, S. Hamida, B. Cherradi, A. Raihani And H. Moujahid, "Performance Evaluation of Transfer Learning Technique for Automatic Detection of Patients with COVID-19 on X-Ray Images," 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), 2020, pp. 1-6.
SH Kassania, PH Kassanib, MJ Wesolowskic, “Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach”, Biometrics and Biomedical Engineering, Elsevier, 2021, vol.41, no.3, pp. 867-879.
V Bolon-Canedo, B Remeseiro, “Feature selection in image analysis: a survey”, Artificial Intelligence Review, Springer 2020, vol. 53, pp. 2905–2931.
R. Miikkulainen, J Liang, E Meyerson, A Rawal, “Evolving deep neural networks”, Artifical Intelligence in the Age of Neural Networks and Brain Computing, Academic Press, 2019, pp.293-312.
A Zargari Khuzani, M Heidari, SA Shariati, “COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images”, Scientific Repots, Nature, 2021, vol.11, no. 9887.
M Shafiq‐ul‐Hassan, GG Zhang, K Latifi, “Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels”, Scientific Reports, Nature, 2018, vol.8, no. 10545.
M Toğaçar, B Ergen, Z Cömert, “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches”, Computers in Biology and Medicine, Elsevier 2020, vol.121, 103805.
SH Kassania, PH Kassanib, MJ Wesolowskic, “Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach”, Biometrics and Biomedical Engineering, Elsevier 2021, vol.41, no.3, pp. 867-879.
E. -S. M. El-Kenawy et al., "Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification," in IEEE Access, 2021, vol. 9, pp. 36019-36037.
K. K. Singh and A. Singh, "Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network," in Big Data Mining and Analytics, 2021, vol. 4, no. 2, pp. 84-93.
B. King, S. Barve, A. Ford and R. Jha, "Unsupervised Clustering of COVID-19 Chest X-Ray Images with a Self-Organizing Feature Map," 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), 2020, pp. 395-398.
ALA Dalal, MAA Al-qaness, Z Cai, EA Alawamy, “IDA: Improving distribution analysis for reducing data complexity and dimensionality in hyperspectral images”, Pattern Recogniton, Elsevier 2023, vol.134, no. 109096.
R Mostafiz, MS Uddin, MM Reza, MM Rahman, “Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features”, Journal of King Saud University - Computer and Information Sciences, Elsevier 2022, vol.34, no.6, Part B, pp. 3226-3235.
U Muhammad, MZ Hoque, M Oussalah, “SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images”, Lnowledge Based Systems, Elsevier 2022, vol.241, 108207.
I. Ahmad, M. Basheri, M. J. Iqbal and A. Rahim, "Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection," in IEEE Access 2018, vol. 6, pp. 33789-33795.
P. Padilla, M. Lopez, J. M. Gorriz, J. Ramirez, D. Salas-Gonzalez and I. Alvarez, "NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease," in IEEE Transactions on Medical Imaging, 2012 vol. 31, no. 2, pp. 207-216
G. Rathee, S. Garg, G. Kaddoum, Y. Wu, D. N. K. Jayakody and A. Alamri, "ANN Assisted-IoT Enabled COVID-19 Patient Monitoring," in IEEE Access, vol. 9, pp. 42483-42492.
C. Zhou, J. Song, S. Zhou, Z. Zhang and J. Xing, "COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images," in IEEE Access, 2021, vol. 9, pp. 81902-8191.
S. K. Zhou et al., "A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises," in Proceedings of the IEEE, 2021, vol. 109, no. 5, pp. 820-838.
S. Tiwari, P. Chanak and S. K. Singh, "A Review of the Machine Learning Algorithms for Covid-19 Case Analysis," in IEEE Transactions on Artificial Intelligence.
T. Sercu, C. Puhrsch, B. Kingsbury and Y. LeCun, "Very deep multilingual convolutional neural networks for LVCSR," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4955-4959.
MA Kızrak, Z Müftüoğlu, T Yıldırım, “Limitations and challenges on the diagnosis of COVID-19 using radiology images and deep learning”, Data Science for COVID-19, Computational Perspectives , Science Direct 2021, pp.91-115.
K. Sanjar, A. Rehman, A. Paul and K. JeongHong, "Weight Dropout for Preventing Neural Networks from Overfitting," 2020 8th International Conference on Orange Technology (ICOT), Daegu, Korea (South), 2020, pp. 1-4.
L Borawar, R Kaur, “ResNet: Solving Vanishing Gradient in Deep Networks”, Proceedings of International Conference on Recent Trends in Computing, Springer 2023, pp 235–247.
M. A. Awal, M. Masud, M. S. Hossain, A. A. -M. Bulbul, S. M. H. Mahmud and A. K. Bairagi, "A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data," in IEEE Access, vol. 9, pp. 10263-10281, 2021.
M Canayaz, S Şehribanoğlu, R Özdağ, “COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms”, Neural Computing, Springer, 2022, vol.34, pp.5349–5365.
F. Rustam et al., "COVID-19 Future Forecasting Using Supervised Machine Learning Models," in IEEE Access, vol. 8, pp. 101489-101499, 2020.
W Chen, P Wang, J Cheng, “Towards mixed-precision quantization of neural networks via constrained optimization”, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5350-5359.
S Ballı, “Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods”, Chaos, Solitons & Fractals, Elsevier, 2021, vol.142, 110512
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