ABSTRACT
The accurate and early diagnosis of lung cancer
and its pathological types is a significant task in the area of
oncology, as the accuracy of the diagnosis has a direct effect on
the treatment plan. The Computed Tomography (CT) imaging
analysis is a key step in the evaluation of lungs; however, the
analysis of images is generally affected by the variability of
human analysis and the complex and heterogeneous nature of
lung tissues. Lung cancer can be divided into two types:
NonSmall Cell Lung Cancer (NSCLC) and Small Cell Lung
Cancer (SCLC), which are biologically distinct and require
completely different treatment plans. The inaccurate or late
diagnosis often leads to the progression of cancer to an
advanced stage, with a greater possibility of invasion of the
adjacent organs and lower survival rates, thus highlighting the
importance of early diagnosis and accurate characterization of
lesions. Although the conventional radiomics-based approach
offers a non-invasive approach to extract handcrafted texture
and shape features from the CT images, the generalization
performance of such approaches is generally limited. To
address these challenges and approach, this research work
proposes an artificial intelligence-assisted diagnostic method
that employs deep learning-based volumetric analysis for
effective feature representation and lesion localization and part.
An improved three-dimensional convolutional neural network
model is proposed using attention mechanisms, which is applied
to efficiently analyze the spatial context and structural patterns
of the pulmonary nodules texture. Deep features are extracted
from the segmented tumor areas, which are then aggregated
with radiomic features for efficient subtype prediction. In
addition, uncertainty estimation and explainable techniques are
applied to further improve the clinical credibility of the
prediction results. By combining multi-organ segmentation,
hierarchical feature learning, and probabilistic classification,
the proposed method supports early diagnosis of lung cancer,
subtype classification, and clinical decision-making. The
combination of Al-assisted diagnostic systems has the potential
to improve screening efficiency that enables personalized
treatment planning, and further assist in improving survival
rates in lung cancer treatment.
KEYWORDS
REFERENCES
- [1] R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2016,” CA: A Cancer Journal for Clinicians, vol. 66, no. 1, pp. 7–30, Jan. 2016.
- [2] A. Wang, H. Wang, Y. Liu, M. Zhao, H. Zhang, Z. Lu, Y. Fang, X. Chen, and G. Liu, “The prognostic value of PD–L1 expression for nonsmall cell lung cancer patients: A meta-analysis,” European Journal of Surgical Oncology, vol. 41, no. 4, pp. 450–456, Apr. 2015.
- [3] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Global Edition, New York, NY, USA: Pearson, 2018, pp. 964–968.
- [4] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
- [5] K. He, X. Zhang, and S. Ren, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
- [6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2017.
- [7] T. F. A. van der Ouderaa, I. Isgum, W. B. Veldhuis, and B. D. de Vos, “Deep group-wise variational diffeomorphic image registration,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Springer, 2020, pp. 24–34.
- [8] F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods, vol. 18, no. 2, pp. 203–211, Feb. 2021.
- [9] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Cambridge, MA, USA: MIT Press, 2016, ch. 11, pp. 326–366.
- [10] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
- [11] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
- [12] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4700–4708.