A Dual-Modality Deep Learning Framework for COVID-19 Detection with Interpretability Using Chest X-Ray and CT Imaging

R. Arvind; Manoj Challa

doi:10.48084/etasr.15824

Authors

R. Arvind Department of Computer Science and Engineering, Gopalan College of Engineering and Management, Bengaluru, India | Visvesvaraya Technological University, Belagavi, Karnataka, India
Manoj Challa Department of Computer Science and Engineering, Gopalan College of Engineering and Management, Bengaluru, India | Visvesvaraya Technological University, Belagavi, Karnataka, India

Volume: 16 | Issue: 1 | Pages: 32089-32096 | February 2026 | https://doi.org/10.48084/etasr.15824

Received: 27 October 2025 | Revised: 8 November 2025, 16 November 2025, and 12 December 2025 | Accepted: 13 December 2025 | Online: 29 December 2025

Corresponding author: R. Arvind

Abstract

The present study presents a comprehensive deep learning framework for automatically diagnosing COVID-19 from chest X-rays and CT scans. A balanced, multi-source dataset comprising three groups, which include normal cases, COVID-19 cases, and pneumonia cases, is produced by deploying the proposed method. It compares an optimized AlexNet with a customized Convolutional Neural Network (CNN) using transfer learning. The methodology involves combining patient-level data, adding more data, and conducting a detailed analysis that includes statistical significance testing and 5-fold cross-validation. An independent dataset (BIMCV-COVID19+) is used for external validation to assess generalizability. Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps address concerns about ease of understanding by showing the regions used for diagnosis. The study also tests the effectiveness of the EfficientNet and Vision Transformer (ViT) models. With an accuracy of 99.1%, the ViT outperforms earlier models in clinical screening scenarios with constrained processing resources. The current study highlights the benefits of using this AI-assisted method in actual radiology operations and emphasizes the significance of maintaining thorough records of the datasets.

Keywords:

COVID19, deep learning, transfer learning, AlexNet, CNN, vision transformers, EfficientNet, X-ray, CT-scan image, Grad-CAM, medical imaging

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References

J. P. Cohen, P. Morrison, and L. Dao, "COVID-19 Image Data Collection." arXiv, 2020.

T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, "Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-ray Images," Computers in Biology and Medicine, vol. 121, Jun. 2020, Art. no. 103792. DOI: https://doi.org/10.1016/j.compbiomed.2020.103792

A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, and A. Mohammadi, "Application of Deep Learning Technique to Manage COVID-19 in Routine Clinical Practice Using CT Images: Results of 10 Convolutional Neural Networks," Computers in Biology and Medicine, vol. 121, Jun. 2020, Art. no. 103795. DOI: https://doi.org/10.1016/j.compbiomed.2020.103795

H. Gunraj, L. Wang, and A. Wong, "COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images," Frontiers in Medicine, vol. 7, Dec. 2020, Art. no. 608525. DOI: https://doi.org/10.3389/fmed.2020.608525

T. Chen et al., "A Vision Transformer Machine Learning Model for COVID-19 Diagnosis Using Chest X-ray Images," Healthcare Analytics, vol. 5, Jun. 2024, Art. no. 100332. DOI: https://doi.org/10.1016/j.health.2024.100332

M. R. Naidji and Z. Elberrichi, "A Novel Hybrid Vision Transformer CNN for COVID-19 Detection from ECG Images," Computers, vol. 13, no. 5, Apr. 2024, Art. no. 109. DOI: https://doi.org/10.3390/computers13050109

T.-A. Pham and V.-D. Hoang, "Chest X-ray Image Classification Using Transfer Learning and Hyperparameter Customization for Lung Disease Diagnosis," Journal of Information and Telecommunication, vol. 8, no. 4, pp. 587–601, Oct. 2024. DOI: https://doi.org/10.1080/24751839.2024.2317509

Y. Sun et al., "COVID-19 Diagnosis Based on Swin Transformer Model with Demographic Information Fusion and Enhanced Multi-Head Attention Mechanism," Expert Systems with Applications, vol. 243, Jun. 2024, Art. no. 122805. DOI: https://doi.org/10.1016/j.eswa.2023.122805

M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in 36th International Conference on Machine Learning, Long Beach, CA, USA, June 2019, Art. no. 97.

S. Jing, H. Kun, Y. Xin, and H. Juanli, "Optimization of Deep-Learning Network Using ResNet50 Based Model for Corona Virus Disease (COVID-19) Histopathological Image Classification," in 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), Changchun, China, Feb. 2022, pp. 992–997. DOI: https://doi.org/10.1109/EEBDA53927.2022.9744883

C. K. Kim et al., "An Automated COVID-19 Triage Pipeline Using Artificial Intelligence Based on Chest Radiographs and Clinical Data," npj Digital Medicine, vol. 5, no. 1, Jan. 2022, Art. no. 5. DOI: https://doi.org/10.1038/s41746-021-00546-w

S. Velu, "An Efficient, Lightweight MobileNetV2-based Fine-Tuned Model for COVID-19 Detection Using Chest X-ray Images," Mathematical Biosciences and Engineering, vol. 20, no. 5, pp. 8400–8427, 2023. DOI: https://doi.org/10.3934/mbe.2023368

A. Marefat, M. Marefat, J. Hassannataj Joloudari, M. A. Nematollahi, and R. Lashgari, "CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact Convolutional Transformers," Frontiers in Public Health, vol. 11, Feb. 2023, Art. no. 1025746. DOI: https://doi.org/10.3389/fpubh.2023.1025746

S. Mohammed, F. Alkinani, and Y. Hassan, "Automatic Computer Aided Diagnostic for COVID-19 Based on Chest X-Ray Image and Particle Swarm Intelligence," International Journal of Intelligent Engineering and Systems, vol. 13, no. 5, pp. 63–73, Oct. 2020. DOI: https://doi.org/10.22266/ijies2020.1031.07

S. Saleem and M. I. Sharif, "An Integrated Deep Learning Framework Leveraging NASNet and Vision Transformer with MixProcessing for Accurate and Precise Diagnosis of Lung Diseases." arXiv, 2025. DOI: https://doi.org/10.1016/j.slast.2026.100394

P. Padmavathi and N. Ganesan, "LungNet-ViT: Vision Transformer Approach for COVID-19 Diagnosis Using Chest Radiographs," Journal of Diagnostic Imaging, vol. 45, no. 3, pp. 189–198, 2025.

R. J. Mohammed et al., "A Robust Hybrid Machine and Deep Learning-based Model for Classification and Identification of Chest X-ray Images," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16212–16220, Oct. 2024. DOI: https://doi.org/10.48084/etasr.7828

X. Yang, X. He, J. Zhao, Y. Zhang, S. Zhang, and P. Xie, "COVID-CT-Dataset: A CT Scan Dataset about COVID-19." arXiv, 2020.

X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, "ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 3462–3471. DOI: https://doi.org/10.1109/CVPR.2017.369

M. de la I. Vayá et al., "BIMCV COVID-19+: A Large Annotated Dataset of RX and CT Images from COVID-19 patients." arXiv, 2020.

R. Arvind, "COVID-19 Chest X-Ray Dataset." Github, Dec. 2025, [Online]. Available: https://github.com/Arvind0202/Covid_19_Dataset.