A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans – Scientific Reports

1. Sun, W., Zheng, B. & Qian, W. Computer aided lung cancer diagnosis with deep learning algorithms. In Medical Imaging 2016: Computer-Aided Diagnosis vol. 9785, 241–248 (SPIE, 2016).

2. Zhou, Z.-H., Jiang, Y., Yang, Y.-B. & Chen, S.-F. Lung cancer cell identification based on artificial neural network ensembles. Artif. Intell. Med. 24, 25–36 (2002).

   Google Scholar 

3. Nie, L. et al. Disease inference from health-related questions via sparse deep learning. IEEE Trans. Knowl. Data Eng. 27, 2107–2119 (2015).

   Google Scholar 

4. Nie, L. et al. Beyond doctors: Future health prediction from multimedia and multimodal observations. In Proceedings of the 23rd ACM International Conference on Multimedia 591–600 (2015).

5. Dhaware, B. U. & Pise, A. C. Lung cancer detection using bayasein classifier and fcm segmentation. In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT) 170–174 (IEEE, 2016).

6. Silva, G. L. F. D., Carvalho Filho, A. O. D., Silva, A. C., Paiva, A. C. D. & Gattass, M. Taxonomic indexes for differentiating malignancy of lung nodules on ct images. Res. Biomed. Eng. 32, 263–272 (2016).

   Google Scholar 

7. Rattan, S., Kaur, S., Kansal, N. & Kaur, J. An optimized lung cancer classification system for computed tomography images. In 2017 Fourth International Conference on Image Information Processing (ICIIP) 1–6 (IEEE, 2017).

8. Saba, T., Khan, M. A., Rehman, A. & Marie-Sainte, S. L. Region extraction and classification of skin cancer: A heterogeneous framework of deep cnn features fusion and reduction. J. Med. Syst. 43, 289 (2019).

   Google Scholar 

9. Kuruvilla, J. & Gunavathi, K. Lung cancer classification using neural networks for ct images. Comput. Methods Programs Biomed. 113, 202–209 (2014).

   Google Scholar 

10. Kumar, D., Wong, A. & Clausi, D. A. Lung nodule classification using deep features in ct images. In 2015 12th Conference on Computer and Robot Vision 133–138 (IEEE, 2015).

11. Detterbeck, F. C., Boffa, D. J., Kim, A. W. & Tanoue, L. T. The eighth edition lung cancer stage classification. Chest 151, 193–203 (2017).

    Google Scholar 

12. Ayshath Thabsheera, A., Thasleema, T. & Rajesh, R. Lung cancer detection using ct scan images: A review on various image processing techniques. Data Analytics and Learning: Proceedings of DAL 2018 413–419 (2018).

13. Lakshmanaprabu, S., Mohanty, S. N., Shankar, K., Arunkumar, N. & Ramirez, G. Optimal deep learning model for classification of lung cancer on ct images. Futur. Gener. Comput. Syst. 92, 374–382 (2019).

    Google Scholar 

14. Khan, S. A. et al. Lungs nodule detection framework from computed tomography images using support vector machine. Microsc. Res. Tech. 82, 1256–1266 (2019).

    Google Scholar 

15. Vasanthi, K. & Kumar, N. B. An efficient lung image classification using gda based feature reduction and tree classifier. In Handbook of Multimedia Information Security: Techniques and Applications 645–666 (Springer, 2019).

16. Nasrullah, N. et al. Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors 19, 3722 (2019).

    Google Scholar 

17. Ali, A., Shahbaz, H. & Damaševičius, R. xcvit: Improved vision transformer network with fusion of cnn and xception for skin disease recognition with explainable ai. Comput. Mater. Continua 83 (2025).

18. Nasir, I. M. et al. An optimized approach for breast cancer classification for histopathological images based on hybrid feature set. Curr. Med. Imaging Rev. 17, 136–147 (2021).

    Google Scholar 

19. Nasir, I. M., Alrasheedi, M. A. & Alreshidi, N. A. Mfan: Multi-feature attention network for breast cancer classification. Mathematics 12, 3639 (2024).

    Google Scholar 

20. Yousafzai, S. N., Nasir, I. M., Tehsin, S., Fitriyani, N. L. & Syafrudin, M. Fltrans-net: Transformer-based feature learning network for wheat head detection. Comput. Electron. Agric. 229, 109706 (2025).

    Google Scholar 

21. Yousafzai, S. N. et al. Multi-stage neural network-based ensemble learning approach for wheat leaf disease classification. IEEE Access (2025).

22. Yousafzai, S. N. et al. Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification. PeerJ Comput. Sci. 11, e3018 (2025).

    Google Scholar 

23. Alzaidi, M. S. A. et al. An efficient fusion network for fake news classification. Mathematics 12, 3294 (2024).

    Google Scholar 

24. Alqadi, B. S. et al. Transfer learning driven fake news detection and classification using large language models. Sci. Rep. 15, 28490 (2025).

    Google Scholar 

25. Ali, A. et al. Towards improved fake news detection using a hybrid roberta and metadata enhanced xgboost model. Sci. Rep. 16, 1967 (2025).

    Google Scholar 

26. Toor, M. S. et al. An optimized weighted-voting-based ensemble learning approach for fake news classification. Mathematics 13, 449 (2025).

    Google Scholar 

27. Fernandes, S. L., Rajinikanth, V. & Kadry, S. A hybrid framework to evaluate breast abnormality using infrared thermal images. IEEE Consum. Electron. Mag. 8, 31–36 (2019).

    Google Scholar 

28. Acharya, U. R. et al. Automated detection of Alzheimer’s disease using brain mri images-a study with various feature extraction techniques. J. Med. Syst. 43, 302 (2019).

    Google Scholar 

29. Amin, J., Sharif, M., Yasmin, M. & Fernandes, S. L. Big data analysis for brain tumor detection: Deep convolutional neural networks. Futur. Gener. Comput. Syst. 87, 290–297 (2018).

    Google Scholar 

30. Liaqat, A. et al. Automated ulcer and bleeding classification from wce images using multiple features fusion and selection. J. Mech. Med. Biol. 18, 1850038 (2018).

    Google Scholar 

31. Rajinikanth, V., Satapathy, S. C., Fernandes, S. L. & Nachiappan, S. Entropy based segmentation of tumor from brain mr images: A study with teaching learning based optimization. Pattern Recogn. Lett. 94, 87–95 (2017).

    Google Scholar 

32. Ranjan, R., Arya, R., Fernandes, S. L., Sravya, E. & Jain, V. A fuzzy neural network approach for automatic k-complex detection in sleep eeg signal. Pattern Recogn. Lett. 115, 74–83 (2018).

    Google Scholar 

33. Satapathy, S. C., Fernandes, S. L. & Lin, H. Stroke lesion segmentation and analysis using entropy/otsu’s function: A study with social group optimization. Curr. Bioinform. 14, 305–313 (2019).

    Google Scholar 

34. Wound, I. S. Shannon’s entropy and watershed algorithm based technique to inspect. In Smart Intelligent Computing and Applications: Proceedings of the Second International Conference on SCI 2018 Volume 2, vol. 105, 23 (Springer, 2018).

35. Raja, N. S. M., Fernandes, S. L., Dey, N., Satapathy, S. C. & Rajinikanth, V. Contrast enhanced medical mri evaluation using tsallis entropy and region growing segmentation. J. Ambient. Intell. Humaniz. Comput. 15, 961–972 (2024).

    Google Scholar 

36. Naqi, S., Sharif, M., Yasmin, M. & Fernandes, S. L. Lung nodule detection using polygon approximation and hybrid features from ct images. Curr. Med. Imaging 14, 108–117 (2018).

    Google Scholar 

37. Sakshiwala & Singh, M. P. A new framework for multi-scale cnn-based malignancy classification of pulmonary lung nodules. J. Ambient Intell. Humaniz. Comput.14, 4675–4683 (2023).

38. Xu, X. et al. Multi-scale supervised contrastive learning for benign-malignant classification of pulmonary nodules in chest ct scans. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) 1–4 (IEEE, 2023).

39. Lima, T., Luz, D., Oseas, A., Veras, R. & Araújo, F. Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features. Multimed. Tools Appl. 82, 42977–42993 (2023).

    Google Scholar 

40. Roy, R., Mazumdar, S. & Chowdhury, A. S. Adgan: Attribute-driven generative adversarial network for synthesis and multiclass classification of pulmonary nodules. IEEE Trans. Neural Netw. Learn. Syst. 35, 2484–2495 (2022).

    Google Scholar 

41. Ghosal, S. S., Sarkar, I. & El Hallaoui, I. Lung nodule classification using convolutional autoencoder and clustering augmented learning method (calm). In HSDM@ WSDM 19–26 (2020).

42. Singh, D. P., Banerjee, T., Kour, P., Swain, D. & Narayan, Y. Cicada (ucx): A novel approach for automated breast cancer classification through aggressiveness delineation. Comput. Biol. Chem. 115, 108368 (2025).

    Google Scholar 

43. Singh, D. P., Kour, P., Banerjee, T. & Swain, D. A comprehensive review of various machine learning and deep learning models for anti-cancer drug response prediction: Comparative analysis with existing state of the art methods. Arch. Comput. Methods Eng. 1–25 (2025).

44. Banerjee, T. et al. A novel hybrid deep learning approach combining deep feature attention and statistical validation for enhanced thyroid ultrasound segmentation. Sci. Rep. 15, 27207 (2025).

    Google Scholar 

45. Banerjee, T. Electromagnetic interaction algorithm (eia)-based feature selection with adaptive kernel attention network (akattnet) for autism spectrum disorder classification. Int. J. Dev. Neurosci. 85, e70034 (2025).

    Google Scholar 

46. Banerjee, T. et al. Pyramidal attention-based t network for brain tumor classification: A comprehensive analysis of transfer learning approaches for clinically reliable and reliable ai hybrid approaches. Sci. Rep. 15, 28669 (2025).

    Google Scholar 

47. Singh, D. P. et al. A comprehensive study on deep learning models for the detection of diabetic retinopathy using pathological images. Arch. Comput. Methods Eng. 1–30 (2025).

48. Banerjee, T. et al. A novel unified inception-u-net hybrid gravitational optimization model (uigo) incorporating automated medical image segmentation and feature selection for liver tumor detection. Sci. Rep. 15, 29908 (2025).

    Google Scholar 

49. Banerjee, T. Comparing bipartite convoluted and attention-driven methods for skin cancer detection: A review of explainable ai and transfer learning strategies. Arch. Comput. Methods Eng. 1–25 (2025).

50. Yousafzai, S. N., Nasir, I. M., Tehsin, S. & Khan, J. A. Mra-net: Multiscale residual attention network for multiclass Alzheimer disease classification. In 2024 5th International Conference on Innovative Computing (ICIC) 1–8 (IEEE, 2024).

51. Thaljaoui, A. et al. Explainable skin cancer diagnosis with parallel attention mechanism for segmentation and classification. Biomed. Signal Process. Control 113, 109159 (2026).

    Google Scholar 

52. Mukherjee, P. et al. A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. Nat. Mach. Intell. 2, 274–282 (2020).

    Google Scholar 

53. Sun, R., Pang, Y. & Li, W. Efficient lung cancer image classification and segmentation algorithm based on an improved swin transformer. Electronics 12, 1024 (2023).

    Google Scholar 

54. Chen, J., Ma, Q. & Wang, W. A lung cancer detection system based on convolutional neural networks and natural language processing. In 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) 354–359 (IEEE, 2021).

55. Yousafzai, S. N. et al. A fusion framework of transformer and cnn for non-small cell lung cancer classification. In International Conference on Smart Systems and Emerging Technologies 162–173 (Springer, 2024).

56. Bangare, S. L. et al. Computer-aided lung cancer detection and classification of ct images using convolutional neural network. In Computer Vision and Internet of Things 247–262 (Chapman and Hall/CRC, 2022).

57. Al-Huseiny, M. et al. Transfer learning with googlenet for detection of lung cancer. Indones. J. Electr. Eng. Comput. Sci. (2021).

58. Al-Yasriy, H. F., Al-Husieny, M. S., Mohsen, F. Y., Khalil, E. A. & Hassan, Z. S. Diagnosis of lung cancer based on ct scans using cnn. In IOP Conference Series: Materials Science and Engineering vol. 928 022035 (IOP Publishing, 2020).

59. Banerjee, T. Towards automated and reliable lung cancer detection in histopathological images using dy-fspan: A feature-summarized pyramidal attention network for explainable ai. Comput. Biol. Chem. 108500 (2025).

60. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).

61. Kaiser, L. et al. One model to learn them all. arXiv preprint arXiv:1706.05137 (2017).

62. Liu, Z. et al. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision 10012–10022 (2021).

63. Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1251–1258 (2017).

64. Szegedy, C., Ioffe, S., Vanhoucke, V. & Alemi, A. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence vol. 31 (2017).

65. Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 4700–4708 (2017).

66. Kareem, H. F., AL-Husieny, M. S., Mohsen, F. Y., Khalil, E. A. & Hassan, Z. S. Evaluation of svm performance in the detection of lung cancer in marked ct scan dataset. Indones. J. Electr. Eng. Comput. Sci. 21, 1731 (2021).

    Google Scholar 

67. Malaviya, N., Rahevar, M., Virani, A., Ganatra, A. & Bhuva, K. Lvit: Vision transformer for lung cancer detection. In 2023 International Conference on Artificial Intelligence and Smart Communication (AISC) 93–98 (IEEE, 2023).

68. Solyman, S. & Schwenker, F. Lung tumor detection and recognition using deep convolutional neural networks. In Pan African Conference on Artificial Intelligence 79–91 (Springer, 2022).

69. Uddin, A. H. et al. Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures. Heliyon10 (2024).

70. Inbasakaran, G. & Ruth, J. A. Clinical-ready cnn framework for lung cancer classification: Systematic optimization for healthcare deployment with enhanced computational efficiency. Intell.-Based Med. 100292 (2025).

Download references