Abstract

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and early detection is critical for improving patient survival rates. This study presents an advanced CT scan–based image analysis pipeline for the reliable detection and classification of pulmonary nodules. The proposed method combines image preprocessing, segmentation, and feature extraction with deep learning classification to improve accuracy and robustness. We address challenges such as class imbalance, domain shift, and inter-class similarity—particularly between benign and normal cases—by applying targeted augmentation, class-balanced losses, and vessel-suppression techniques. Experimental evaluation on benchmark datasets shows that our approach achieves high accuracy and recall, especially for malignant cases, while minimizing false negatives in benign detection. The results highlight the potential of our method for integration into computer-aided diagnosis systems to support radiologists in clinical decision-making.

References

• A. A. Syed, W. Fatima, and R. Wajahat, “Comparative analysis of learning algorithms for lung cancer identification,” Indian Journal of Science and Technology, 2018. doi: 10.17485/ijst/2018/v11i27/130707.
• J. K. Dash, S. Mukhopadhyay, M. K. Garg, N. Prabhakar, and N. Khandelwal, “Multi-classifier framework for lung tissue classification,” in 2014 IEEE Students’ Technology Symposium, 2014, pp. 264–269. doi: 10.1109/TechSym.2014.6808058.
• Ö. Günaydin, M. Günay, and Ö. S¸ engel, “Comparison of lung cancer de- tection algorithms,” in 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, 2019, pp. 1–4. doi: 10.1109/EBBT.2019.8741826.
• S. Makaju, P. W. C. Prasad, A. Alsadoon, A. K. Singh, and A. Elchouemi, “Lung cancer detection using CT scan images,” Procedia Computer Science, vol. 125, pp. 107–114, 2018. doi: 10.1016/j.procs.2017.12.016.
• Q. Zhang and X. J. I. A. Kong, “Design of automatic lung nod- ule detection system based on multi-scene deep learning framework,” IEEE Access, vol. 8, pp. 90380–90389, 2020. doi: 10.1109/AC- CESS.2020.2994657.
• W. Ausawalaithong, A. Thirach, S. Marukatat, and T. Wilaiprasitporn, “Automatic lung cancer prediction from chest X-ray images using the deep learning approach,” in 2018 11th Biomedical Engineering Inter- national Conference (BMEiCON), 2018, pp. 1–5. doi: 10.1109/BME- iCON.2018.8609997.
• M. Li, J. Li, J. Li, X. Chen, S. Li, and Z. Zhang, “Research on the auxiliary classification and diagnosis of lung cancer subtypes based on histopathological images,” IEEE Access, vol. 9, pp. 53687–53707, 2021. doi: 10.1109/ACCESS.2021.3070060.
• H. Yu, Z. Zhou, and Q. J. I. A. Wang, “Deep learning assisted prediction of lung cancer on computed tomography images using the adaptive hierarchical heuristic mathematical model,” IEEE Access, vol. 8, pp. 86400–86410, 2020. doi: 10.1109/ACCESS.2020.2993275.
• M. A. Thanoon, M. A. Zulkifley, M. A. A. MohdZainuri, and S. R. J. D. Abdani, “A review of deep learning techniques for lung cancer screening and diagnosis based on CT images,” Diagnostics, vol. 13, no. 16, p. 2617, 2023. doi: 10.3390/diagnostics13162617.
• M. Mamun, M. I. Mahmud, M. Meherin, and A. Abdelgawad, “Lcdctcnn: Lung cancer diagnosis of CT scan images using CNN based model,” in 2023 10th International Conference on Signal Pro- cessing and Integrated Networks (SPIN), 2023, pp. 205–212. doi: 10.1109/SPIN56868.2023.10098905.
• M. Altarawneh, “Lung cancer detection using image processing tech- niques,” Leonardo Electronic Journal of Practices and Technologies, Aug. 2012.
• S. K. Lakshmanaprabu, S. N. Mohanty, K. Shankar, N. Arunkumar, and G. Ramirez, “Optimal deep learning model for classification of lung cancer on CT images,” Future Generation Computer Systems, vol. 92, pp. 374–382, 2019. doi: 10.1016/j.future.2018.10.029.
• M. C. S. Mez, A. C. S. Mez, and T. E. P. E. Cengiz, “Lung cancer detection by hybrid learning method applying SMOTE technique,” Gazi University Journal of Science Part C: Design and Technology, vol. 10, no. 4, pp. 1098–1110, 2022. doi: 10.35378/gujsc.1079587.
• S. M. Ashhar, S. S. Mokri, A. A. AbdRahni, A. B. Huddin, N. Zulkarnain, N. A. Azmi, and T. Mahaletchumy, “Comparison of deep learning convolutional neural network (CNN) architectures for CT lung cancer classification,” International Journal of Advanced Technology and Engineering Exploration, vol. 8, no. 74, pp. 126–132, 2021. doi: 10.19101/IJATEE.2021.874.
• E. Cengil and A. Cinar, “A deep learning based approach to lung cancer identification,” in 2018 International Conference on Artifi- cial Intelligence and Data Processing (IDAP), 2018, pp. 1–5. doi: 10.1109/IDAP.2018.8620844.
• https://www.kaggle.com/datasets/dishantrathi20/ ct-scan-images-for-lung-cancer.