Department of Electrical, Electronics and Communication Engineering, GITAM School of Technology, GITAM, Deemed to Be University Doddaballapura, Bengaluru, Karnataka, India.
In order to effectively treat pneumonia, which is still a major worldwide health problem, rapid and precise diagnosis is essential. This paper introduces an ensemble strategy to improve pneumonia identification using chest X-ray images (CXM), utilising developments in deep learning. We propose an Ensemble Deep Neural Networks (EDNN), comprising cascaded ShuffleNet and Support Vector Machines (SVM), to harness diverse features and improve classification performance. The ensemble method combines the strengths of multiple models, mitigating individual weaknesses and enhancing overall diagnostic accuracy. Implementation is carried out using Python, and the proposed approach achieves an impressive accuracy of 97.89% on benchmark datasets. Through extensive experimentation and validation on benchmark datasets, our approach demonstrates superior performance compared to individual models and existing state-of-the-art methods. Additionally, we provide insights into the interpretability of ensemble predictions, enhancing the transparency and trustworthiness of automated pneumonia detection systems. The proposed ensemble framework holds promise for robust and reliable pneumonia detection in clinical settings, facilitating timely interventions and improving patient outcomes.
Keywords
Chest X-ray Images, Ensemble Deep Neural Networks, Support Vector Machines, Pneumonia Identification.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Senthil Kumar S, Pravin Kumar M, Karthick S and Nilabar Nisha U;
Methodology: Senthil Kumar S and Pravin Kumar M;
Software: Karthick S and Nilabar Nisha U;
Data Curation: Senthil Kumar S and Pravin Kumar M;
Writing- Original Draft Preparation: Senthil Kumar S, Pravin Kumar M, Karthick S and Nilabar Nisha U;
Visualization: Senthil Kumar S and Pravin Kumar M;
Investigation: Karthick S and Nilabar Nisha U;
Supervision: Senthil Kumar S and Pravin Kumar M;
Validation: Karthick S and Nilabar Nisha U;
Writing- Reviewing and Editing: Senthil Kumar S, Pravin Kumar M, Karthick S and Nilabar Nisha U;
All authors reviewed the results and approved the final version of the manuscript.
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The authors would like to thank to the reviewers for nice comments on the manuscript.
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Senthil Kumar S
Department of Electrical and Electronics Engineering, K.S.R. College of Engineering, Tiruchengode, Tamil Nadu, India.
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Cite this article
Senthil Kumar S, Pravin Kumar M, Karthick S and Nilabar Nisha U, “Enhanced Pneumonia Detection Using Ensembled Deep Neural Networks”, Journal of Machine and Computing, pp. 1152-1159, April 2025, doi: 10.53759/7669/jmc202505091.
