Abstract
The early detection of pulmonary nodules using computer-aided diagnosis (CAD) systems is very essential in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning approach to improve the classification accuracy of pulmonary nodules in computed tomography (CT) images. Our proposed CNN-5CL (convolutional neural network with 5 convolutional layers) approach uses an 11-layer convolutional neural network (with 5 convolutional layers) for automatic feature extraction and classification. The proposed method is evaluated using LIDC/IDRI images. The proposed method is implemented in the Python platform, and the performance is evaluated with metrics such as accuracy, sensitivity, specificity, and receiver operating characteristics (ROC). The results show that the proposed method achieves accuracy, sensitivity, specificity, and area under the roc curve (AUC) of 98.88%, 99.62%, 93.73%, and 0.928, respectively. The proposed approach outperforms various other methods such as Naïve Bayes, K-nearest neighbor, support vector machine, adaptive neuro fuzzy inference system methods, and also other deep learning-based approaches.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Liu J, Jiang H, Gao M (2017) An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images. J Med Syst 41:30–43
Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31:198–211
Gong J, Liu J, Wang L, Zheng B, Nie S (2016) Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier. European Journal of Medical Physics 2016:1502–1509
Keming Mao, Zhuofu Deng, (2016) Lung nodule image classification based on local difference pattern and combined classifier, computational and mathematical methods in medicine, 2016: 1–7.
Han H, Li L (2015) (2015) Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. In Proceedings of IEEE International Journal of Biomedical and Health Informatics 19(2):648–659
Anam Tariq M, Usman Akram M, Javed Y (2013) Lung nodule detection in CT images using neuro fuzzy classifier. IEEE Computational Intelligence in Medical Imaging 2013:1–8
Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554
Song Q, Zhao L, Luo X, Dou X (2017) Using deep learning for classification of lung nodules on computed tomography images. Journal of Healthcare Engineering 2017:1–7
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, Proceedings of the IEEE International Conference on Learning. Representations 2014:1–14
Krizhevsky A, Sutskever I, Hinton G. E (2012) ImageNet classification with deep convolutional neural networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012: 1097–1105.
Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ (2014) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets and Therapy 2015:2015–2022
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.580–587.
He K, Zhang X, Ren, S, Sun J (2016). Deep residual learning for image recognition, Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770–778.
Kumar D, Wong A, Clausi D. A (2015) Lung nodule classification using deep features in CT images, 12th IEEE Conference on Computer and Robot Vision (CRV), 2015: 133–138.
Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification, Proceedings of the 24th International Conference on Information Processing in Medical Imaging, pp. 588–599.
Shin HC, Roth HR, Gao M (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285–1298
Li W, Cao P, Zhao D, Wang J (2016) Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Methods Med 2016:1–7
Jiang H, Ma H, Qian W, Gao M, Li Y (2017) An automatic detection system of lung nodule based on multi-group patch based deep learning network. IEEE Journal of Biomed Health Informatics 22:1227–1237
Choi WJ, Choi TS (2013) Automated pulmonary nodule detection system in computed tomography images: a hierarchical block classification approach. Entropy 15:507–523
Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113:202–209
LIDC-IDRI, (https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI).
LeCun Y, Kavukcuoglu K (2010) Farabet C (2010) Convolutional networks and applications in vision, Proceedings of. IEEE International Symposium on Circuits and Systems (ISCAS) 2010:253–256
Chen H, Hwang BJ, Shyr J, Liu T (2020) The effect of different deep network architectures upon CNN-based gaze tracking. Algorithms 2020:1–18
Zhang G-W, Jien Kato Yu, Wang KM (2014) Multi-stage deep convolutional learning for people re-identification. Computer System Science & Engineering 2014:265–274
Zheng Qiumei; Tan Dan; Wang Fenghua (2020) Improved Convolutional Neural Network Based on Fast Exponentially Linear Unit Activation Function. IEEE Access 2020:151359–151367
Chen J, Hua Z, Wang J, Cheng S (2017) A convolutional neural network with dynamic correlation pooling. International Conference on Computational Intelligence and Security 2017:496–499
Vatalaro M, Moposita T, Strangio S, Trojman L, Vladimirescu A, Lanuzza M, Crupi F (2021) A low-voltage, low-power reconfigurable current-mode softmax circuit for analog neural networks. Electronics 2021:1–11
Armato SG, 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. Med Phys 38:915–931
Krewer H, Geiger B, Hall L. O (2013) Effect of texture features in computer aided diagnosis of pulmonary nodules in low dose computed tomography, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013:3887–3891.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Manickavasagam, R., Selvan, S. & Selvan, M. CAD system for lung nodule detection using deep learning with CNN. Med Biol Eng Comput 60, 221–228 (2022). https://doi.org/10.1007/s11517-021-02462-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-021-02462-3