Evaluation Of 3dcnn Architecture For Classification Of Lung Diseases.

Authors

  • T.Evangeline Dhivya
  • R.Balasubramanian

Keywords:

3DCNN, 3D-VGG16, 3D-AlexNet, 3D-GoogleNet, 3D-VoxResNet

Abstract

Globally, infectious disease-related illnesses have long been a problem. Lung Cancer, COVID-19 and pneumonia (bacterial and viral pneumonia), all impact the lungs and result in millions of fatalities annually. In every situation, opportunities for improved care can be created by early identification and diagnosis. Computerized Tomography (CT) is utilized, nevertheless, to identify lung disease and identify symptoms. Even for doctors, using various imaging modalities is a challenging and time-consuming activity that is subject to varying opinions from different observers. Thus, there is considerable interest in creating algorithms that can automatically distinguish between people with lung disease and those who are healthy. Our research compares different 3DCNN architecture to accurately detect a subset of lung disorders using patient respiratory images, with the goal of improving the diagnostic accuracy of respiratory ailments. We demonstrated that 3D-VoxResNet produces superior outcomes when compared to other frameworks like 3D-VGG16,3D-AlexNet,3D-GoogleNet..

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References

Bhuvaneswari, C., Aruna, P., & Loganathan, D. (2014). Classification of lung diseases by image processing techniques using computed tomography images. International Journal of Advanced Computer Research, 4(1), 87.

2. Boban, B. M., & Megalingam, R. K. (2020, July). Lung diseases classification based on machine learning algorithms and performance evaluation. In 2020 international conference on communication and signal processing (ICCSP) (pp. 0315-0320). IEEE.

3. Goyal, S., & Singh, R. (2023). Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3239-3259.

4. Srivastava, V., & Purwar, R. K. (2020). Classification of CT scan images of lungs using deep convolutional neural network with external shape-based features. Journal of digital imaging, 33(1), 252-261.

5. Gangeh, M. J., Sørensen, L., Shaker, S. B., Kamel, M. S., De Bruijne, M., & Loog, M. (2010). A texton-based approach for the classification of lung parenchyma in CT images. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010: 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III 13 (pp. 595-602). Springer Berlin Heidelberg.

6. Gugulothu, V. K., & Balaji, S. (2024). An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques. Multimedia Tools and Applications, 83(1), 1041-1061.

7. Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., Shin, H. C., ... & Mollura, D. J. (2018). Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6(1), 1-6.

8. Khan, M. A., Rajinikanth, V., Satapathy, S. C., Taniar, D., Mohanty, J. R., Tariq, U., & Damaševičius, R. (2021). VGG19 network assisted joint segmentation and classification of lung nodules in CT images. Diagnostics, 11(12), 2208. https://doi.org/10.3390/diagnostics11122208

9. Farhan, A. M. Q., & Yang, S. (2023). Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm. Multimedia Tools and applications, 82(25), 38561-38587.

10. Marentakis, P., Karaiskos, P., Kouloulias, V., Kelekis, N., Argentos, S., Oikonomopoulos, N., & Loukas, C. (2021). Lung cancer histology classification from CT images based on radiomics and deep learning models. Medical & biological engineering & computing, 59, 215-226.

11. Bhuvaneswari, C., Aruna, P., & Loganathan, D. (2014). A new fusion model for classification of the lung diseases using genetic algorithm. Egyptian Informatics Journal, 15(2), 69-77.

12. Khan, M. A., Rubab, S., Kashif, A., Sharif, M. I., Muhammad, N., Shah, J. H., ... & Satapathy, S. C. (2020). Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection. Pattern Recognition Letters, 129, 77-85.

13. Kumar, S., Bhagat, V., Sahu, P., Chaube, M. K., Behera, A. K., Guizani, M., ... & Alsamhi, S. H. (2024). A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases. Computer Methods and Programs in Biomedicine, 243, 107911.

14. Yasar, H., & Ceylan, M. (2024). Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images. Cognitive Computation, 16(4), 1806-1833.

15. Vijay, P., Jena, A., & Gnanavel, S. (2024, July). Detection and classification of human lung diseases using convolutional neural networks. In AIP Conference Proceedings (Vol. 3075, No. 1). AIP Publishing.

16. Sabry, A. H., Bashi, O. I. D., Ali, N. N., & Al Kubaisi, Y. M. (2024). Lung disease recognition methods using audio-based analysis with machine learning. Heliyon.10(24), 2405-8440

17. Quasar, S. R., Sharma, R., Mittal, A., Sharma, M., Agarwal, D., & de La Torre Díez, I. (2024). Ensemble methods for computed tomography scan images to improve lung cancer detection and classification. Multimedia Tools and Applications, 83(17), 52867-52897.

18. D.Arul Suresh, J.Jude Moses Anto Devakanth, Dr.R.Balasubramanian (2022) A Novel Feature Extraction Technique for classification of Tuberculosis Chest X-Ray Images using Deep Learning Models. NeuroQuantology, 3733-3756

19. Lai, Y., Liu, X., Hou, F., Han, Z., Su, N., Du, D., ... & Wu, Y. (2024). Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images. Journal of X-Ray Science and Technology, (Preprint), 1-16.

20. Pham, T. A., & Hoang, V. D. (2024). Chest X-ray image classification using transfer learning and hyperparameter customization for lung disease diagnosis. Journal of Information and Telecommunication, 1-15.

21. Pal, T., Goswami, B., & Barnwal, R. P. (2024). Handling Segmentation and Classification Problems in Deep Learning for Identification of Interstitial Lung Disease. In Machine Learning and Deep Learning Techniques for Medical Image Recognition (pp. 128-151). CRC Press.

22. J. Jude Moses Anto Devakanth, Dr. R. Balasubramanian, Arul Suresh(2023).A Modified Moth Flame Optimization Algorithm for Multi-level Classification of COVID-19 from Tuberculosis and Pneumonia Chest X Ray images using Deep learning. Journal of chemical health risk 13(3),177-1197

23. Sneha Balannolla et al “Detection and Classification of Lung Carcinoma using CT scans” Journal of Physics (2022). doi:10.1088/1742-6596/2286/1/012011.

24. Huseyin Polat and Homay Danaei Mehr “Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture” applied science (2019):9,940.

25. Ahmed, J. et al. (2020). COPD Classification in CT Images Using a 3D Convolutional Neural Network. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden https://doi.org/10.1007/978-3-658-29267-6_8.

26. Large COVID-19 CT scan slice dataset Available online:

https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset.

27. Lung Cancer Detection dataset available online: https://github.com/DorsaRoh/LungAI.

28. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. (1997) 9:1735–80.

29. H. Chen, Q. Dou, L. Yu, J. Qin, and P. Heng. Voxresnet: Deep voxelwise residual networks for brain segmentation from 3d mr images. NeuroImage, 170:446–455, 2018.

30. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Process. Syst. 2012, 60, 84–90.

31. Iandola, F.N.; Han, S.; Moskewicz, M.W.; Ashraf, K.; Dally, W.J.; Keutzer, K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv 2016, arXiv:1602.07360.

32. Zhang, X.; Zhou, X.; Lin, M.; Sun, J. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. arXiv 2017, arXiv:1707.01083v2.

33. Zhou, B.; Khosla, A.; Lapedriza, A.; Torralba, A.; Oliva, A. Places: An image database for ddeep scene understanding. arXiv 2016, arXiv:1610.02055. [CrossRef].

34. leUllah, N.; Marzougui, M.; Ahmad, I.; Chelloug, S.A. DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs. Electronics 2023, 12, 1860.

35. Hussein, F.; Mughaid, A.; AlZu’bi, S.; El-Salhi, S.M.; Abuhaija, B.; Abualigah, L.; Gandomi, A.H. Hybrid clahe-cnn deep neural.

36. Avanzato R, Beritelli F, Lombardo A, Ricci C. Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis. Sensors (Basel). 2024 Feb 1;24(3):958. doi: 10.3390/s24030958. PMID: 38339678; PMCID: PMC10857717.

37. Uddin, Jia. "Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images." Designs 8.2 (2024): 27.

38. Zhang, Pinzhi, Alagappan Swaminathan, and Ahmed Abrar Uddin. "Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks." Frontiers in Medicine 10 (2023): 1269784.

39. Hong Liu1 & Haichao Cao & Enmin “Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification.” Journal of Digital Imaging (2020). https://doi.org/10.1007/s10278-020-00372-8.

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Published

2025-10-15

How to Cite

1.
Dhivya T, R.Balasubramanian R. Evaluation Of 3dcnn Architecture For Classification Of Lung Diseases. J Neonatal Surg [Internet]. 2025 Oct. 15 [cited 2026 Apr. 14];14(32S):10677-89. Available from: https://jneonatalsurg.com/index.php/jns/article/view/10061

Issue

Vol. 14 No. 32S (2025): Journal of Neonatal Surgery

Section

Original Article

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