Fusing Deep Sequential Information and Ensemble Learning for Accurate COVID-19 Classification
Keywords:
COVID-19, Deep sequential learning, Ensemble learning, Bi-GRU, Random Forest, Radiological imagingAbstract
In the realm of medical image analysis, accurate classification of COVID-19 from radiological imaging remains a critical challenge. Leveraging the complementary strengths of deep sequential learning and ensemble methods, this research presents a novel approach that amalgamates Bidirectional Gated Recurrent Units (Bi-GRU) with Random Forest to achieve precise COVID-19 classification with Adam optimization. The proposed method capitalizes on the distinctive features extracted from chest X-rays and CT scans, exploiting the inherent sequential dependencies in these multi-modal imaging modalities. The Bi-GRU component serves as a potent feature extractor, enabling the model to capture intricate spatial and temporal patterns within the images. Subsequently, the extracted features are harnessed by the Random Forest ensemble, harnessing its ability to refine decision boundaries and enhance generalization. Empirical evaluation of the developed framework underscores its efficacy. Leveraging a comprehensive dataset, the approach achieves remarkable classification accuracy rates of 98.87% for chest X-ray images and 89.21% for CT scans. This substantiates the capacity of the proposed fusion model to discern even nuanced distinctions within the complex radiological data. The synergy between Bi-GRU and Random Forest not only significantly elevates classification performance but also contributes to interpretable insights. Through feature importance analysis, the model uncovers salient regions and temporal dynamics in the images that play pivotal roles in accurate COVID-19 classification. This research extends the horizons of medical image analysis by showcasing the potential of integrating deep sequential information with ensemble learning methodologies. The presented approach not only advances the current state-of-the-art in COVID-19 classification but also offers a versatile framework applicable to other medical image analysis tasks.
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References
G. Bargshady, X. Zhou, P. D. Barua, R. Gururajan, Y. Li, and U. R. Acharya, “Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images,” Pattern Recognit. Lett., vol. 153, pp. 67–74, 2022, doi: 10.1016/j.patrec.2021.11.020.
A. H. Barshooi and A. Amirkhani, “A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images,” Biomed. Signal Process. Control, vol. 72, no. PA, p. 103326, 2022, doi: 10.1016/j.bspc.2021.103326.
G. Celik, “Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network[Formula presented],” Appl. Soft Comput., vol. 133, p. 109906, 2023, doi: 10.1016/j.asoc.2022.109906.
S. P. Das, S. Mitra, and B. U. Shankar, “Collective intelligent strategy for improved segmentation of COVID-19 from CT,” Expert Syst. Appl., vol. 235, no. April 2023, p. 121099, 2024, doi: 10.1016/j.eswa.2023.121099.
V. Khetani, Y. Gandhi, S. Bhattacharya, S. N. Ajani, and S. Limkar, “INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Cross-Domain Analysis of ML and DL : Evaluating their Impact in Diverse Domains,” vol. 11, pp. 253–262, 2023.
M. Gour and S. Jain, “Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network,” Biocybern. Biomed. Eng., vol. 42, no. 1, pp. 27–41, 2022, doi: 10.1016/j.bbe.2021.12.001.
M. Hemalatha, “A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection,” Expert Syst. Appl., vol. 210, no. June, p. 118227, 2022, doi: 10.1016/j.eswa.2022.118227.
S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. Jamalipour Soufi, “Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning,” Med. Image Anal., vol. 65, 2020, doi: 10.1016/j.media.2020.101794.
G. Jain, D. Mittal, D. Thakur, and M. K. Mittal, “A deep learning approach to detect Covid-19 coronavirus with X-Ray images,” Biocybern. Biomed. Eng., vol. 40, no. 4, pp. 1391–1405, 2020, doi: 10.1016/j.bbe.2020.08.008.
V. S. Rohila, N. Gupta, A. Kaul, and D. K. Sharma, “Deep learning assisted COVID-19 detection using full CT-scans,” Internet of Things (Netherlands), vol. 14, p. 100377, 2021, doi: 10.1016/j.iot.2021.100377.
S. M. J. Jalali, M. Ahmadian, S. Ahmadian, R. Hedjam, A. Khosravi, and S. Nahavandi, “X-ray image based COVID-19 detection using evolutionary deep learning approach,” Expert Syst. Appl., vol. 201, no. March, p. 116942, 2022, doi: 10.1016/j.eswa.2022.116942.
A. Kumar, “RYOLO v4-tiny: A deep learning based detector for detection of COVID and Non-COVID Pneumonia in CT scans and X-RAY images,” Optik (Stuttg)., vol. 268, no. June, p. 169786, 2022, doi: 10.1016/j.ijleo.2022.169786.
H. Hosseinzadeh, “Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images,” Biomed. Signal Process. Control, vol. 75, no. February, p. 103595, 2022, doi: 10.1016/j.bspc.2022.103595.
M. Emin Sahin, “Deep learning-based approach for detecting COVID-19 in chest X-rays,” Biomed. Signal Process. Control, vol. 78, no. June, p. 103977, 2022, doi: 10.1016/j.bspc.2022.103977.
L. Fang and X. Wang, “COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images,” Biocybern. Biomed. Eng., vol. 42, no. 3, pp. 977–994, 2022, doi: 10.1016/j.bbe.2022.07.009.
M. Kumar, D. Shakya, V. Kurup, and W. Suksatan, “COVID-19 prediction through X-ray images using transfer learning-based hybrid deep learning approach,” Mater. Today Proc., vol. 51, pp. 2520–2524, 2022, doi: 10.1016/j.matpr.2021.12.123.
Z. Cao, J. Huang, X. He, and Z. Zong, “BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images,” Knowledge-Based Syst., vol. 258, p. 110040, 2022, doi: 10.1016/j.knosys.2022.110040.
N. Narayan Das, N. Kumar, M. Kaur, V. Kumar, and D. Singh, “Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays,” Irbm, vol. 43, no. 2, pp. 114–119, 2022, doi: 10.1016/j.irbm.2020.07.001.
H. I. Hussein, A. O. Mohammed, M. M. Hassan, and R. J. Mstafa, “Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images,” Expert Syst. Appl., vol. 223, no. March, p. 119900, 2023, doi: 10.1016/j.eswa.2023.119900.
A. Deeb, A. Debow, S. Mansour, and V. Shkodyrev, “COVID-19 diagnosis with Deep Learning: Adjacent-pooling CTScan-COVID-19 Classifier Based on ResNet and CBAM,” Biomed. Signal Process. Control, vol. 86, no. PC, p. 105285, 2023, doi: 10.1016/j.bspc.2023.105285.
N. Ghassemi et al., “Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning,” Appl. Soft Comput., vol. 144, p. 110511, 2023, doi: 10.1016/j.asoc.2023.110511.
R. Soundrapandiyan, H. Naidu, M. Karuppiah, M. Maheswari, and R. C. Poonia, “AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images,” Comput. Electr. Eng., vol. 108, no. April, p. 108711, 2023, doi: 10.1016/j.compeleceng.2023.108711.
Rahman Tawsifur, “COVID-19 Radiography Database | Kaggle,” Kaggle, vol. 4, no. March. p. 2021, 2021, [Online]. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/activity%0Ahttps://www.kaggle.com/tawsifurrahman/covid19-radiography-database.
O. Pavia, “COVID-19 CT scans | Kaggle,” Coronacases.org. 2020, [Online]. Available: https://www.kaggle.com/andrewmvd/covid19-ct-scans.
Kakulapati, V., & Jayanthiladevi, A. . (2023). Self-aware COVID-19 AI Approach (SIntL-CoV19) by Integrating Infected Scans with Internal Behavioral Analysis. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 87–93. https://doi.org/10.17762/ijritcc.v11i3.6205
Rohokale, M. S., Dhabliya, D., Sathish, T., Vijayan, V., & Senthilkumar, N. (2021). A novel two-step co-precipitation approach of CuS/NiMn2O4 heterostructured nanocatalyst for enhanced visible light driven photocatalytic activity via efficient photo-induced charge separation properties. Physica B: Condensed Matter, 610 doi:10.1016/j.physb.2021.412902
Steffy, A. D. . (2021). Dimensionality Reduction Based Diabetes Detection Using Feature Selection and Machine Learning Architectures. Research Journal of Computer Systems and Engineering, 2(2), 45:50. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/32
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