ItemConference Paper

Ensemble Deep Learning for COVID-19 Detection Using Multi-Modal Medical Imaging

Title
Ensemble Deep Learning for COVID-19 Detection Using Multi-Modal Medical Imaging
Creator
Babu Kumar, S.; Vinodha, D.; Geetha, P.; Jenefa, J.; Anoop, G.L.; Santhrupth, B.C.
Description
The COVID-19 pandemic has had a profound impact worldwide This work proposes a deep ensemble learning model incorporating multi-modal inputs, i.e., CT scans and Xrays, to classify the cases into COVID-19, Viral Pneumonia, or Normal. Employing an ensemble average voting approach from three different CNN models InceptionV3, DenseNet-169, and Xception the suggested methodology is highly accurate and reliable. Preprocessing methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) improve data quality, and Local Interpretable Model-Agnostic Explanations (LIME) allow interpretable prediction through identification of major image features driving classifications. The ensemble model suggested attains an accuracy of 99.64%, outperforming single models, with precision at 99.50%, recall at 99.73%, and an F1-score of 99.61%, which makes it very reliable for detecting COVID-19. Comparative analysis shows that our ensemble method performs better than individual CNN architectures, such as Xception (99.18%), ResNet101 (98.95%), and DenseNet201 (98.83%), which showcases its better diagnostic performance. 2025 IEEE.
Source
Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Classification; CLEHE; Covid-19; Ensemble Average Voting; Ensemble Model; InceptionV3; LIME
Coverage
Babu Kumar S., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Vinodha D., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Geetha P., SRM Institute of Science and Technology, Department of Computational Intelligence, Kattankulathur, India; Jenefa J., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Anoop G.L., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India; Santhrupth B.C., CHRIST University, Department of Computer Science and Engineering, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152118-9;
Format
online
Language
English
Type
Conference paper

Citation

Babu Kumar, S.; Vinodha, D.; Geetha, P.; Jenefa, J.; Anoop, G.L.; Santhrupth, B.C., “Ensemble Deep Learning for COVID-19 Detection Using Multi-Modal Medical Imaging,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26157.

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