International Journal of Emerging Research in Engineering, Science, and Management
Vol. 5, Issue 1, pp. 42-68, Jan-Mar 2026.
https://doi.org/10.58482/ijeresm.v5i1.4

Received: 30 Nov 2025 | Revised: o2 Mar 2026 | Accepted: 08 Mar 2026 | Published: 16 Mar 2026

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Atrous Convolutional Self-Attention-Based Capsule Network for Lung Disease Classification

Praveena Kakarla

Vimala C

Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu Dt, Tamil Nadu 603203, India.

Abstract: Lung diseases remain a major global health concern, affecting the supply of oxygen to other parts of the body. There are several types of lung diseases, including asthma, COPD, and pneumonia. Numerous methods have been developed to identify these lung diseases; however, they still have several shortcomings, including long processing times, complex structures, and poor classification accuracy. To address these issues, a lung disease classification system is developed using the proposed method. First, pre-processing techniques are applied to improve image quality by reducing noise and enhancing contrast using Modified Histogram Equalization and Cross-guided Bilateral Filtering. The images are collected from the NIH ChestX-ray dataset. Next, Extended Lyrebird Optimization is utilized to select the optimal features, and the Squeeze-Excited DenseNet201 (SE-DenseNet201) model is employed for feature extraction. Finally, an Atrous Convolutional Self-Attention-based Capsule Network model is utilized for classification, and the Kookaburra Optimization Algorithm is employed for hyperparameter tuning. The proposed approach is evaluated using the NIH ChestX-ray dataset and achieves an accuracy of 92.70%, with 92.13% precision and 92.99% recall.

Keywords: Lung disease classification, Chest X-ray imaging, DenseNet201, Capsule network, Feature selection, Metaheuristic optimization.

References
  1. F. J. M. Shamrat, S. Azam, A. Karim, K. Ahmed, F. M. Bui, and F. De Boer, “High-Precision Multiclass Classification of Lung Disease Through Customized MobileNetV2 from Chest X-Ray Images,” Computers in Biology and Medicine, vol. 155, p. 106646, Feb. 2023. https://doi.org/10.1016/j.compbiomed.2023.106646
  2. P. Podder et al., “LDDNET: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases,” Sensors, vol. 23, no. 1, p. 480, Jan. 2023. https://doi.org/10.3390/s23010480
  3. M. Murugappan, A. K. Bourisly, N. B. Prakash, M. G. Sumithra, and U. R. Acharya, “Automated Semantic Lung Segmentation in Chest CT Images Using Deep Neural Network,” Neural Computing and Applications, vol. 35, no. 21, pp. 15343-15364, Apr. 2023. https://doi.org/10.1007/s00521-023-08407-1
  4. J. Weiss et al., “Deep Learning to Estimate Lung Disease Mortality from Chest Radiographs,” Nature Communications, vol. 14, no. 1, p. 2797, May 2023. https://doi.org/10.1038/s41467-023-37758-5
  5. V. Ravi, V. Acharya, and M. Alazab, “A Multichannel EfficientNet Deep Learning-Based Stacking Ensemble Approach for Lung Disease Detection Using Chest X-Ray Images,” Cluster Computing, vol. 26, no. 2, pp. 1181-1203, Jul. 2022. https://doi.org/10.1007/s10586-022-03664-6
  6. P. Taneja, A. Sharma, and M. Singh, “Enhancing Multi-Class Lung Disease Classification with Bagging-Based Deep Learning Ensembles: A Comparative Study of Five Architectures,” Procedia Computer Science, vol. 260, pp. 209-216, Jan. 2025. https://doi.org/10.1016/j.procs.2025.03.195
  7. H. Malik, T. Anees, A. S. Al-Shamaylehs, S. Z. Alharthi, W. Khalil, and A. Akhunzada, “Deep Learning-Based Classification of Chest Diseases Using X-Rays, CT Scans, and Cough Sound Images,” Diagnostics, vol. 13, no. 17, p. 2772, Aug. 2023. https://doi.org/10.3390/diagnostics13172772
  8. G. M. M. Alshmrani, Q. Ni, R. Jiang, H. Pervaiz, and N. M. Elshennawy, “A Deep Learning Architecture for Multi-Class Lung Diseases Classification Using Chest X-Ray (CXR) Images,” Alexandria Engineering Journal, vol. 64, pp. 923-935, Nov. 2022. https://doi.org/10.1016/j.aej.2022.10.053
  9. H. Malik, T. Anees, M. Din, and A. Naeem, “CDC_Net: Multi-Classification Convolutional Neural Network Model for Detection of COVID-19, Pneumothorax, Pneumonia, Lung Cancer, and Tuberculosis Using Chest X-Rays,” Multimedia Tools and Applications, vol. 82, no. 9, pp. 13855-13880, Sep. 2022. https://doi.org/10.1007/s11042-022-13843-7
  10. Z. Naz, M. U. G. Khan, T. Saba, A. Rehman, H. Nobanee, and S. A. Bahaj, “An Explainable AI-Enabled Framework for Interpreting Pulmonary Diseases from Chest Radiographs,” Cancers, vol. 15, no. 1, p. 314, Jan. 2023. https://doi.org/10.3390/cancers15010314
  11. X. Sun, Z. Song, H. Jiang, Y. Ma, and M. Chen, “Image Classification of Immune Checkpoint Inhibitor–Related Pneumonia in Lung Cancer Patients,” Clinical Imaging, vol. 86, pp. 31-37, Mar. 2022. https://doi.org/10.1016/j.clinimag.2022.03.012
  12. C. Liu, W. Xie, R. Zhao, and M. Pang, “Segmenting Lung Parenchyma from CT Images with Gray Correlation-Based Clustering,” IET Image Processing, vol. 17, no. 6, pp. 1658-1667, Jan. 2023. https://doi.org/10.1049/ipr2.12744
  13. K. Wang, Y. An, J. Zhou, Y. Long, and X. Chen, “A Novel Multi-Level Feature Selection Method for Radiomics,” Alexandria Engineering Journal, vol. 66, pp. 993-999, Nov. 2022. https://doi.org/10.1016/j.aej.2022.10.069
  14. J. R. Astley et al., “A Hybrid Model- and Deep Learning-Based Framework for Functional Lung Image Synthesis from Multi-Inflation CT and Hyperpolarized Gas MRI,” Medical Physics, vol. 50, no. 9, pp. 5657-5670, Mar. 2023. https://doi.org/10.1002/mp.16369
  15. T. Wanasinghe, S. Bandara, S. Madusanka, D. Meedeniya, M. Bandara, and I. D. L. T. Díez, “Lung Sound Classification With Multi-Feature Integration Utilizing Lightweight CNN Model,” IEEE Access, vol. 12, pp. 21262-21276, 2024. https://doi.org/10.1109/ACCESS.2024.3361943
  16. N. S. Haider and A. K. Behera, “Computerized Lung Sound Based Classification of Asthma and Chronic Obstructive Pulmonary Disease (COPD),” Journal of Applied Biomedicine, vol. 42, no. 1, pp. 42-59, Dec. 2021. https://doi.org/10.1016/j.bbe.2021.12.004
  17. L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, “A Neural Network-Based Method for Respiratory Sound Analysis and Lung Disease Detection,” Applied Sciences, vol. 12, no. 8, p. 3877, Apr. 2022. https://doi.org/10.3390/app12083877
  18. Y. Zhang et al., “Research on Lung Sound Classification Model Based on Dual-Channel CNN-LSTM Algorithm,” Biomedical Signal Processing and Control, vol. 94, p. 106257, Mar. 2024. https://doi.org/10.1016/j.bspc.2024.106257
  19. M. Kaveh, M. S. Mesgari, and B. Saeidian, “Orchard Algorithm (OA): A New Meta-Heuristic Algorithm for Solving Discrete and Continuous Optimization Problems,” Mathematics and Computers in Simulation, vol. 208, pp. 95-135, Jan. 2023. https://doi.org/10.1016/j.matcom.2022.12.027
  20. A. Kabiraj, T. Meena, K. Tadepalli, and S. Roy, “An Explainable Weakly Supervised Model for Multi-Disease Detection and Localization from Thoracic X-Rays,” Applied Soft Computing, vol. 166, p. 112139, Aug. 2024. https://doi.org/10.1016/j.asoc.2024.112139
  21. N. W. Asnake, A. O. Salau, and A. M. Ayalew, “X-Ray Image-Based Pneumonia Detection and Classification Using Deep Learning,” Multimedia Tools and Applications, vol. 83, no. 21, pp. 60789-60807, Jan. 2024. https://doi.org/10.1007/s11042-023-17965-4
  22. M. Kholiavchenko, I. Pershin, B. Maksudov, T. Mustafaev, Y. Yuan, and B. Ibragimov, “Gaze-Based Attention to Improve the Classification of Lung Diseases,” Medical Imaging 2022: Image Processing, Mar. 2022, p. 10. https://doi.org/10.1117/12.2612767
  23. F. Wang, S. Li, S. Li, Y. Gao, S. Li, and S. Li, “Computed Tomography-Based Artificial Intelligence in Lung Disease—Chronic Obstructive Pulmonary Disease,” MedComm – Future Medicine, vol. 3, no. 1, Feb. 2024. https://doi.org/10.1002/mef2.73
  24. X. Deng et al., “COPD Stage Detection: Leveraging the Auto-Metric Graph Neural Network with Inspiratory and Expiratory Chest CT Images,” Medical & Biological Engineering & Computing, vol. 62, no. 6, pp. 1733-1749, Feb. 2024. https://doi.org/10.1007/s11517-024-03016-z
  25. A. Souid, N. Sakli, and H. Sakli, “Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2,” Applied Sciences, vol. 11, no. 6, p. 2751, Mar. 2021. https://doi.org/10.3390/app11062751
  26. S. Kim, B. Rim, S. Choi, A. Lee, S. Min, and M. Hong, “Deep Learning in Multi-Class Lung Diseases' Classification on Chest X-ray Images,” Diagnostics, vol. 12, no. 4, p. 915, Apr. 2022. https://doi.org/10.3390/diagnostics12040915
  27. S. Z. Y. Zaidi, M. U. Akram, A. Jameel, and N. S. Alghamdi, “A Deep Learning Approach for the Classification of TB from NIH CXR Dataset,” IET Image Processing, vol. 16, no. 3, pp. 787-796, 2021. https://doi.org/10.1049/ipr2.12385
  28. M. Nawaz, T. Nazir, J. Baili, M. A. Khan, Y. J. Kim, and J.-H. Cha, “CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model,” Diagnostics, vol. 13, no. 2, p. 248, Jan. 2023. https://doi.org/10.3390/diagnostics13020248
  29. J.-X. Wu, P.-Y. Chen, C.-M. Li, Y.-C. Kuo, N.-S. Pai, and C.-H. Lin, “Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images,” IEEE Access, vol. 8, pp. 105886-105902, 2020. https://doi.org/10.1109/ACCESS.2020.3000186
  30. D. C. Lepcha, B. Goyal, and A. Dogra, “Image Fusion Based on Cross Bilateral and Rolling Guidance Filter Through Weight Normalization,” The Open Neuroimaging Journal, vol. 13, no. 1, pp. 51-61, Dec. 2020. https://doi.org/10.2174/1874440002013010051
  31. S. Samuel, R. S. Ochawar, and M. S. S. Rukmini, “Hybrid Deep Autoencoder Network Based Adaptive Cross Guided Bilateral Filter for Motion Artifacts Correction and Denoising from MRI,” The Imaging Science Journal, vol. 72, no. 1, pp. 76-91, Apr. 2023. https://doi.org/10.1080/13682199.2023.2196494
  32. K. Verma, G. Sikka, A. Swaraj, S. Kumar, and A. Kumar, “Classification of COVID-19 on Chest X-Ray Images Using Deep Learning Model with Histogram Equalization and Lung Segmentation,” SN Computer Science, vol. 5, no. 4, Mar. 2024. https://doi.org/10.1007/s42979-024-02695-7
  33. H. A. Sanghvi, R. H. Patel, A. Agarwal, S. Gupta, V. Sawhney, and A. S. Pandya, “A Deep Learning Approach for Classification of COVID and Pneumonia Using DenseNet-201,” International Journal of Imaging Systems and Technology, vol. 33, no. 1, pp. 18-38, Sep. 2022. https://doi.org/10.1002/ima.22812
  34. H. Mkindu, L. Wu, and Y. Zhao, “Lung Nodule Detection of CT Images Based on Combining 3D-CNN and Squeeze-and-Excitation Networks,” Multimedia Tools and Applications, vol. 82, no. 17, pp. 25747-25760, Mar. 2023. https://doi.org/10.1007/s11042-023-14581-0
  35. Purba Daru Kusuma, “Focus and Shake Algorithm: A New Stochastic Optimization Employing Strict and Randomized Dimension Mappings,” International Journal of Intelligent Engineering & Systems, vol. 17, no. 3, pp. 551-562, 2024. https://doi.org/10.22266/ijies2024.0630.43
  36. D. Dahiya, “COVID-19 Disease Prediction Utilizing Dilated Convolution Neural Network Based Levy Flight Tunicate Swarm Optimization,” Wireless Personal Communications, vol. 131, no. 3, pp. 1515-1528, May 2023. https://doi.org/10.1007/s11277-023-10505-1
  37. Minki Kim and Byoung-Dai Lee, “Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network,” Sensors, vol. 21, no. 2, p. 369, 2021. https://doi.org/10.3390/s21020369
  38. F. Karim et al., “Towards an Effective Model for Lung Disease Classification,” Applied Soft Computing, vol. 124, p. 109077, May 2022. https://doi.org/10.1016/j.asoc.2022.109077
  39. Mohammad Dehghani, Eva Trojovská, Pavel Trojovský, and Om Parkash Malik, “OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems,” Biomimetics, vol. 8, no. 6, p. 468, 2023. https://doi.org/10.3390/biomimetics8060468
  40. N. L. Yadav, S. Singh, R. Kumar, and D. K. Nishad, “Transfer Learning with Fuzzy Decision Support for Multi-Class Lung Disease Classification: Performance Analysis of Pre-trained CNN Models,” Scientific Reports, vol. 15, no. 1, p. 35127, Oct. 2025. https://doi.org/10.1038/s41598-025-19114-3
  41. E. Rajasekar, H. Chandra, N. Pears, S. Vairavasundaram, and K. Kotecha, “Lung Image Quality Assessment and Diagnosis Using Generative Autoencoders in Unsupervised Ensemble Learning,” Biomedical Signal Processing and Control, vol. 102, p. 107268, Nov. 2024. https://doi.org/10.1016/j.bspc.2024.107268
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