Fusing Deep Sequential Information and Ensemble Learning for Accurate COVID-19 Classification


  • Manish K. Assudani Raisoni Centre for Research and Innovation G. H. Raisoni University, Amravati, India
  • Neeraj Sahu Raisoni Centre for Research and Innovation G. H. Raisoni University,Amravati, India


COVID-19, Deep sequential learning, Ensemble learning, Bi-GRU, Random Forest, Radiological imaging


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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How to Cite

Assudani, M. K. ., & Sahu, N. . (2023). Fusing Deep Sequential Information and Ensemble Learning for Accurate COVID-19 Classification. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 94–101. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3438



Research Article