Abstract

Lung cancer is the leading cause of cancer-related deaths, and early detection is critical for improving survival. This study presents an explainable deep learning approach for classifying chest CT scans into normal, benign, and malignant categories. Six pretrained convolutional neural networks were evaluated on the public IQ-OTH/NCCD dataset using class-weighted training and light data augmentation. EfficientNet-B0 achieved the best performance, with 99.39% accuracy, a 0.988 macro F1-score, and 0.996 AUC, while correctly identifying all malignant cases.

References

• F. Bray, M. Laversanne, H. Sung, J. Ferlay, R. L. Siegel, I. Soerjomataram, and A. Jemal, “Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 74, no. 3, pp. 229-263, 2024, doi: 10.3322/caac.21834.
• R. L. Siegel, A. N. Giaquinto, and A. Jemal, “Cancer statistics, 2024,” CA: A Cancer Journal for Clinicians, vol. 74, no. 1, pp. 12-49, 2024, doi: 10.3322/caac.21820.
• A. Leiter, R. R. Veluswamy, and J. P. Wisnivesky, “The global burden of lung cancer: current status and future trends,” Nature Reviews Clinical Oncology, vol. 20, no. 9, pp. 624-639, 2023, doi: 10.1038/s41571-023-00798-3.
• W. Quanyang, H. Yao, W. Sicong, Q. Linlin, Z. Zewei, et al., “Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis,” Cancer Medicine, vol. 13, no. 7, art. no. e7140, 2024, doi: 10.1002/cam4.7140.
• G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60-88, 2017, doi: 10.1016/j.media.2017.07.005.
• A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115-118, 2017, doi: 10.1038/nature21056.
• Rashad Bakhshizada, “Heart Disease Prediction using SVM and RF”, International Journal of Science and Research Archive, 2024, 13(02), 1617-1623.
• J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 248-255, 2009.
• R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE International Conference on Computer Vision (ICCV), pp. 618-626, 2017, doi: 10.1109/ICCV.2017.74.
• C. Patr\'icio, J. C. Neves, and L. F. Teixeira, “Explainable deep learning methods in medical image classification: A survey,” ACM Computing Surveys, vol. 56, no. 4, 2023, doi: 10.1145/3625287.
• M. A. Thanoon, M. A. Zulkifley, M. A. A. M. Zainuri, and S. R. Abdani, “A review of deep learning techniques for lung cancer screening and diagnosis based on CT images,” Diagnostics, vol. 13, no. 16, art. no. 2617, 2023, doi: 10.3390/diagnostics13162617.
• L. Wang, “Deep learning techniques to diagnose lung cancer,” Cancers, vol. 14, no. 22, art. no. 5569, 2022, doi: 10.3390/cancers14225569.
• House, J., Bakhshizada, R., Janušonis, S., Metzler, R., & Vojta, T. (2025). Fractional Brownian motion with mean-density interaction: A myopic self-avoiding fractional stochastic process. Physical Review E, 112(3), 034119.
• P. Wu, X. Sun, Z. Zhao, H. Wang, S. Pan, and B. Schuller, “Classification of lung nodules based on deep residual networks and migration learning,” Computational Intelligence and Neuroscience, vol. 2020, art. no. 8975078, 2020, doi: 10.1155/2020/8975078.
• W. Li, S. Yu, R. Yang, Y. Tian, T. Zhu, et al., “Machine learning model of ResNet50-ensemble voting for malignant-benign small pulmonary nodule classification on computed tomography images,” Cancers, vol. 15, no. 22, art. no. 5417, 2023, doi: 10.3390/cancers15225417.
• Y. Zhang, W. Feng, Z. Wu, W. Li, L. Tao, et al., “Deep-learning model of ResNet combined with CBAM for malignant-benign pulmonary nodules classification on computed tomography images,” Medicina, vol. 59, no. 6, art. no. 1088, 2023, doi: 10.3390/medicina59061088.
• A. Saha, S. M. Ganie, P. K. D. Pramanik, R. K. Yadav, S. Mallik, and Z. Zhao, “VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images,” BMC Medical Imaging, vol. 24, 2024, doi: 10.1186/s12880-024-01238-z.
• A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NeurIPS), pp. 1097-1105, 2012.
• H. F. Al-Yasriy, M. S. Al-Husieny, F. Y. Mohsen, E. A. Khalil, and Z. S. Hassan, “Diagnosis of lung cancer based on CT scans using CNN,” in IOP Conference Series: Materials Science and Engineering, vol. 928, no. 2, art. no. 022035, 2020, doi: 10.1088/1757-899X/928/2/022035.
• H. F. Kareem, M. S. Al-Huseiny, F. Y. Mohsen, E. A. Khalil, and Z. S. Hassan, “Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 3, pp. 1731-1738, 2021, doi: 10.11591/ijeecs.v21.i3.pp1731-1738.
• M. S. Al-Huseiny and A. S. Sajit, “Transfer learning with GoogLeNet for detection of lung cancer,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 2, pp. 1078-1086, 2021, doi: 10.11591/ijeecs.v22.i2.pp1078-1086.
• T. I. A. Mohamed, O. N. Oyelade, and A. E. Ezugwu, “Automatic detection and classification of lung cancer CT scans based on deep learning and Ebola optimization search algorithm,” PLOS ONE, vol. 18, no. 8, art. no. e0285796, 2023, doi: 10.1371/journal.pone.0285796.
• J. Abdollahi, “Evaluating LeNet algorithms in classification lung cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases,” arXiv, 2023, arXiv:2305.13333.
• V. R. Nitha and S. S. Vinod Chandra, “ExtRanFS: An automated lung cancer malignancy detection system using extremely randomized feature selector,” Diagnostics, vol. 13, no. 13, art. no. 2206, 2023, doi: 10.3390/diagnostics13132206.
• R. Raza, F. Zulfiqar, M. O. Khan, M. Arif, A. Alvi, M. A. Iftikhar, and T. Alam, “Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images,” Engineering Applications of Artificial Intelligence, vol. 126, art. no. 106902, 2023, doi: 10.1016/j.engappai.2023.106902.
• S. Pathan, T. Ali, P. G. Sudheesh, P. Vasanth Kumar, and D. Rao, “An optimized convolutional neural network architecture for lung cancer detection,” APL Bioengineering, vol. 8, no. 2, art. no. 026121, 2024, doi: 10.1063/5.0208520.
• M. M. Musthafa, I. Manimozhi, T. R. Mahesh, and S. Guluwadi, “Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques,” BMC Medical Informatics and Decision Making, vol. 24, 2024, doi: 10.1186/s12911-024-02553-9.
• S. Majumder, N. Gautam, A. Basu, A. Sau, Z. W. Geem, and R. Sarkar, “MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans,” PLOS ONE, vol. 19, no. 3, art. no. e0298527, 2024, doi: 10.1371/journal.pone.0298527.
• B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2921-2929, 2016, doi: 10.1109/CVPR.2016.319.
• E. Chamseddine, N. Mansouri, M. Soui, and M. Abed, “Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss,” Applied Soft Computing, vol. 129, art. no. 109588, 2022, doi: 10.1016/j.asoc.2022.109588.
• U. K. Ghosh, F. Al Abir, N. Rifaat, S. M. Shovan, A. Sayeed, and M. A. M. Hasan, “Most dominant metabolomic biomarkers identification for lung cancer,” Informatics in Medicine Unlocked, vol. 28, art. no. 100824, 2022.
• D. Das, M. M. Billah, A. D. Nath, N. B. Sharif, and K. K. Mondal, “Breast cancer classification using LGBM and SVM,” International Journal of Science and Research Archive, vol. 7, no. 2, pp. 876-881, 2022.
• R. Bakhshizada, S. A. S. M. Naim, and M. B. N. Shahin, “Detection of dementia using gradient boosting,” International Journal of Computer Engineering and Technology, vol. 14, no. 3, pp. 311-322, 2023.
• H. Alyasriy and M. Al-Huseiny, “The IQ-OTH/NCCD lung cancer dataset,” Mendeley Data, V4, 2023, doi: 10.17632/bhmdr45bh2.4.
• K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
• K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations (ICLR), 2015, arXiv:1409.1556.
• M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. International Conference on Machine Learning (ICML), pp. 6105-6114, 2019.
• S. A. Sabbirul Mohosin Naim; Tanvir Mahmud; Md Hossain (2025) Smart-LungNet for Lung Disease Classification. International Journal of Innovative Science and Research Technology, 10(12), 233-236. https://doi.org/10.38124/ijisrt/25dec139
• Sikder, T. R., Siam, M. A., Melon, M. M. H., Uddin, S. M. M., Mohonta, S. C., & Karim, F. (2023). A Multimodal Data Analytics Framework for Early Cancer Detection Using Genomic, Radiomic, and Clinical Big Data Fusion. Journal of Computer Science and Technology Studies, 5(3), 183-188.
• Md Masum Billah, Rashad Bakhshizada, Denesh Das, Tasmita Tanjim Tanha, Rashedur Rahman, “Brain Tumor Classification using EfficientNet”, International Journal of Computer Applications. 187, 86 (Mar 2026), 66-71. DOI=10.5120/ijca2026926493