Jiajia Li J.J Li
ABSTRACT
Lung cancer, renowned for having the highest global incidence and mortality rates among all cancers, presents a promising avenue for improving survival rates through early detection and precise diagnosis. However, current diagnostic methods relying on the manual interpretation of CT images are susceptible to subjectivity and potential errors. To address this challenge, we introduce an innovative fully convolutional neural network that synergistically integrates multi-scale feature fusion and joint upsampling modules. Our model aims to enhance the precision of lung cancer diagnosis by effectively categorizing benign and malignant pulmonary nodules within CT images. Leveraging a comprehensive dataset comprising 1012 pulmonary nodule samples sourced from LIDC-IDRI, our evaluation reveals exceptional performance metrics. Notably, the model achieves an impressive area under the ROC curve of 97.35%, along with a high accuracy of 94.21%, sensitivity of 93.79%, and specificity of 94.91%.
- Cancer Statistics Center. (n.d.). Retrieved from https://cancerstatisticscenter.cancer.org/.Google Scholar
- American Cancer Society. (n.d.). Key Statistics for Lung Cancer. Retrieved from https://www.cancer.org/cancer/lung-cancer/about/key-statistics.htmlGoogle Scholar
- Oudkerk, M., Devaraj, A., Vliegenthart, R., (2017). European position statement on lung cancer screening. The Lancet Oncology, 18(12), E754-E766. DOI:10.1016/S1470-2045(17)30861-6Google ScholarCross Ref
- Liu, Y., Balagurunathan, Y., & Atwater, T. (2017). Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules. Clinical Cancer Research, 23(6), 1442-1449.Google ScholarCross Ref
- Huang, X. J., Shan, J. J., & Vaidya, V. (2017). Lung nodule detection in CT using 3D convolutional neural networks. In Proceedings of ISBI (pp. 379-383).Google ScholarCross Ref
- Jin, T., Cui, H., Zeng, S., & Wang, X. (2017). Learning deep spatial lung features by 3D convolutional neural network for early cancer detection. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 588-599).Google ScholarCross Ref
- ImageNet. Available online: http://www.image-net.org/ (accessed on 4 February 2019).Google Scholar
- Liu H, Cao H, Song E, Ma G, Xu X, Jin R, Liu C, Hung CC. Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification. J Digit Imaging. 2020 Oct;33(5):1242-1256. doi: 10.1007/s10278-020-00372-8. PMID: 32607905; PMCID: PMC7649841.Google ScholarCross Ref
- Nasrullah N, Sang J, Alam MS, Mateen M, Cai B, Hu H. Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies. Sensors (Basel). 2019 Aug 28;19(17):3722. doi: 10.3390/s19173722. PMID: 31466261; PMCID: PMC6749467.Google ScholarCross Ref
- Liu, K., & Kang, G. (2017). Multiview convolutional neural networks for lung nodule classification. International Journal of Imaging Systems and Technology, 27(1), 12-22.Google ScholarDigital Library
- Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015). Multi-scale Convolutional Neural Networks for Lung Nodule Classification. Information Processing in Medicine, 24, 588-599.Google Scholar
- Zhao, J., Zhang, C., Li, D., (2020). Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules. Journal of Digital Imaging, 33, 869-878.Google ScholarCross Ref
- Hamidian, S., Sahiner, B., Petrick, N., (2017). 3D convolutional neural network for automatic detection of lung nodules in chest CT. In Medical Imaging 2017: Computer-Aided Diagnosis (Vol. 10134, p. 101340L).Google Scholar
- Wu, H., Zhang, J., Huang, K., (2019). Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation. arXiv preprint arXiv:1903.11816.Google Scholar
- Armato, S. G., McLennan, G., Bidaut, L., (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. DOI:10.1118/1.3528204Google ScholarCross Ref
- Zhang, Y., Yang, Z., Lu, H., Zhou, X., Phillips, P., Liu, Q., & Wang, S. (2016). Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation. IEEE Access, 4, 8375-8385. DOI:10.1109/ACCESS.2016.2627347Google ScholarCross Ref
- Kamnitsas, K., Ledig, C., Newcombe, V. F. J., (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61-78. DOI:10.1016/j.media.2016.10.004Google ScholarCross Ref
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778.Google ScholarCross Ref
- Chen, L. C., Papandreou, G., Kokkinos, I., (2014). Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062.Google Scholar
- Chen, L. C., Papandreou, G., Kokkinos, I., (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834-848. DOI:10.1109/TPAMI.2017.2699184Google ScholarCross Ref
- Lyu, J., & Ling, S. H. (2018). Using multi-level convolutional neural network for classification of lung nodules on CT images. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 686-689. DOI:10.1109/EMBC.2018.8512570Google ScholarCross Ref
- Xie, Y., Zhang, J., Xia, Y., Fulham, M., & Zhang, Y. (2018). Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Information Fusion, 42, 102-110. DOI:10.1016/j.inffus.2017.09.007Google ScholarDigital Library
- Causey, J. L., Zhang, J., Ma, S., Jiang, B., Qualls, J. A., Politte, D. G., Prior, F., Zhang, S., & Huang, X. (2018). Highly accurate model for prediction of lung nodule malignancy with CT scans. Scientific Reports, 8, 9286. DOI:10.1038/s41598-018-27475-7Google ScholarCross Ref
- Zhao, J., Zhang, C., Li, D., (2020). Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules. Journal of Digital Imaging, 33, 869-878. DOI:10.1007/s10278-019-00264-7Google ScholarCross Ref
Index Terms
- Advancing Pulmonary Nodule Classification: A Novel Multi-Scale Fusion and Joint Upsampling Strategy using 3D Convolutional Neural Networks
Recommendations
Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline
Highlights- The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and a less invasive chemo or radio therapies. Nodule ...
Abstract Background and ObjectiveThe early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is ...
Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography
AbstractComputed tomography (CT) is widely used to locate pulmonary nodules for preliminary diagnosis of the lung cancer. However, due to high visual similarities between malignant (cancer) and benign (non-cancer) nodules, distinguishing malignant from ...
An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification
Highlights- An efficient 3-D computer-aided classification (CADx) model improved from the split-transform-merge convolution neural network (CNN) architecture is used for ...
Abstract Background and ObjectiveLung cancer has the highest cancer-related mortality worldwide, and lung nodule usually presents with no symptom. Low-dose computed tomography (LDCT) was an important tool for lung cancer detection ...
Comments