Article: Ensemble learning and skip connection-based CNN framework for COVID-19 identification using CXR and CT images Journal: International Journal of Computational Vision and Robotics (IJCVR) 2026 Vol.16 No.3 pp.273 - 294 Abstract: COVID-19 causes a severe deterioration to the respiratory system by infecting the lungs, resulting in high fatality rates. Thus, in order to reduce the mortality rate chest radiographs such as CT and CXR of lungs can be utilised for early identification. The proposed work introduces a novel convolutional neural network architecture TES-Net for performing COVID-19 detection from CT and CXR. The model is based on transfer learning, ensemble learning and skip connections. Transfer learning allows to circumvent the need for lots of new data to train a model, while ensemble learning uses a combination of different individual models to obtain a higher predictive accuracy. Moreover, skip connections are useful in tackling the problem of vanishing gradients. The experimental results are described in terms of different evaluation metrics and compared with several existing CNNs and machine learning classifiers. An ablation study is also conducted to show the significance of different components. Inderscience Publishers - linking academia, business and industry through research

Title: Ensemble learning and skip connection-based CNN framework for COVID-19 identification using CXR and CT images

Authors: Muzammil Khan; Bhavana Singh; Pushpendra Kumar

Addresses: Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology Bhopal, Bhopal – 462003, Madhya Pradesh, India ' Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology Bhopal, Bhopal – 462003, Madhya Pradesh, India ' Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology Bhopal, Bhopal – 462003, Madhya Pradesh, India

Abstract: COVID-19 causes a severe deterioration to the respiratory system by infecting the lungs, resulting in high fatality rates. Thus, in order to reduce the mortality rate chest radiographs such as CT and CXR of lungs can be utilised for early identification. The proposed work introduces a novel convolutional neural network architecture TES-Net for performing COVID-19 detection from CT and CXR. The model is based on transfer learning, ensemble learning and skip connections. Transfer learning allows to circumvent the need for lots of new data to train a model, while ensemble learning uses a combination of different individual models to obtain a higher predictive accuracy. Moreover, skip connections are useful in tackling the problem of vanishing gradients. The experimental results are described in terms of different evaluation metrics and compared with several existing CNNs and machine learning classifiers. An ablation study is also conducted to show the significance of different components.

Keywords: convolutional neural network; CNN; COVID-19; CT scan; CXR; ensemble learning; skip connection; transfer learning.

DOI: 10.1504/IJCVR.2026.153126

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.3, pp.273 - 294

Received: 01 May 2023
Accepted: 25 Nov 2023
Published online: 23 Apr 2026 *

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Title: Ensemble learning and skip connection-based CNN framework for COVID-19 identification using CXR and CT images

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