Revolutionizing Lung Cancer Detection: An Advanced Deep Learning Framework for Superior Accuracy and Early Diagnosis
DOI:
https://doi.org/10.63682/jns.v14i11S.3039Keywords:
Lung cancer, Early detection, Cancer-related deaths, Deep learning framework Convolutional Neural Networks (CNNs), Attention mechanisms, High-resolution medical imaging, CT scans, Malignant lesionsAbstract
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with early detection being critical for improving patient outcomes. This paper presents a novel deep learning framework designed to revolutionize lung cancer detection by achieving superior accuracy and enabling early diagnosis. Leveraging advanced convolutional neural networks (CNNs) and attention mechanisms, the proposed model processes high-resolution medical imaging data, such as CT scans, to identify malignant lesions with unprecedented precision. The framework incorporates innovative preprocessing techniques, ensemble learning, and transfer learning to enhance generalizability and robustness. Evaluated on a large, publicly available dataset, the model demonstrates significant improvements in key performance metrics, including accuracy, precision, recall, and AUC-ROC, outperforming state-of-the-art methods. The results highlight the potential of this framework to assist clinicians in early and accurate lung cancer diagnosis, ultimately improving patient care and survival rates. This research paves the way for future advancements in AI-driven medical diagnostics.
Downloads
Metrics
References
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097-1105.
Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248. https://doi.org/10.1146/annurev-bioeng-071516-044442
Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Setio, A. A. A., et al. (2017). Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging, 35(5), 1160-1169. https://doi.org/10.1109/TMI.2016.2536809
Ardila, D., et al. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954-961. https://doi.org/10.1038/s41591-019-0447-x
Huang, X., et al. (2020). Deep learning for lung cancer diagnosis: A review. IEEE Access, 8, 137895-137910. https://doi.org/10.1109/ACCESS.2020.3011728
Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 30, 5998-6008.
Selvaraju, R. R., et al. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. IEEE International Conference on Computer Vision (ICCV), 618-626. https://doi.org/10.1109/ICCV.2017.74
Wang, X., et al. (2018). Non-local neural networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7794-7803. https://doi.org/10.1109/CVPR.2018.00813
Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249. https://doi.org/10.1002/widm.1249
Tan, C., et al. (2018). A survey on deep transfer learning. International Conference on Artificial Neural Networks (ICANN), 270-279. https://doi.org/10.1007/978-3-030-01424-7_27
He, K., et al. (2016). Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778. https://doi.org/10.1109/CVPR.2016.90
Armato, S. G., et al. (2011). The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38(2), 915-931. https://doi.org/10.1118/1.3528204
National Lung Screening Trial Research Team. (2011). The National Lung Screening Trial: Overview and study design. Radiology, 258(1), 243-253. https://doi.org/10.1148/radiol.10091808
Clark, K., et al. (2013). The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. Journal of Digital Imaging, 26(6), 1045-1057. https://doi.org/10.1007/s10278-013-9622-7
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 60. https://doi.org/10.1186/s40537-019-0197-0
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
ang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19. https://doi.org/10.1145/3298981
Ramachandram, D., & Taylor, G. W. (2017). Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Processing Magazine, 34(6), 96-108. https://doi.org/10.1109/MSP.2017.2738401
Powers, D. M. W. (2011). Evaluation: From precision, recall, and F-measure to ROC, informedness, markedness, and correlation. Journal of Machine Learning Technologies, 2(1), 37-63.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437. https://doi.org/10.1016/j.ipm.2009.03.002
Reddy, S., et al. (2019). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association (JAMIA), 27(3), 491-497. https://doi.org/10.1093/jamia/ocz192
Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0272-7
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
