Document Type : Other


Department of Computer Engineering, University of Mazandaran, Babolsar, Iran.


Deep-learning-based approaches have been extensively used in detecting pulmonary nodules from computer Tomography (CT) scans. In this study, an automated end-to-end framework with a convolution network (Conv-net) has been proposed to detect lung nodules from CT images. Here, boundary regression has been performed by a direct regression method, in which the offset is predicted from a given point. The proposed framework has two outputs; a pixel-wise classification between nodule or normal and a direct regression which is used to determine the four coordinates of the nodule's bounding box. The Loss function includes two terms; one for classification and the other for regression. The performance of the proposed method is compared with YOLOv2. The evaluation has been performed using Lung-Pet-CT-DX dataset. The experimental results show that the proposed framework outperforms the YOLOv2 method. The results demonstrate that the proposed framework possesses high accuracies of nodule localization and boundary estimation.


[1] D. Arenberg, “Update on screening for lung cancer”, Transl. Lung Cancer Res., vol. 8, pp. 77-87, 2019.
[2] W. J.  Choi and T. S. Choi, “Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor”. Comput. Methods Programs Biomed., vol.113, pp. 37-54, 2014.
[3] A. O. F. de Carvalho, W. B. de Sampaio, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, “Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index”. Artif. Intell. Med., vol. 60, pp. 165-177, 2014.
[4] T. Adams, J. Drpinghaus, M. Jacobs, and V. Steinhage, “Automated lung tumor detection and diagnosis in CT scans using texture feature analysis and SVM”. Communication Papers of the Federated Conference on Computer Science and Information Systems, pp. 13–20, 2018.
[5] O. Zinoveva, D. Zinovev, S. A. Siena, D. S. Raicu, J. Furst, and S. G. Armato, “A texture-based probabilistic approach for lung nodule segmentation”. In International Conference Image Analysis and Recognition, pp. 21-30. Springer, Berlin, Heidelberg, June 2011.
[6] A.C. Jirapatnakul,S. V. Fotin, A. P. Reeves,  A.M. Biancardi, D. F. Yankelevitz, and C. I. Henschke, C. I.” Automated nodule location and size estimation using a multi-scale Laplacian of Gaussian filtering approach.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1028-1031, IEEE, 2009.
[7] Y. Tao, L.  Lu, M.  Dewan, A. Y. Chen, J. Corso, J. Xuan, and A. Krishnan, “Multi-level ground glass nodule detection and segmentation in CT lung images”. International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 715-723, Springer, Berlin, Heidelberg, September 2009.
[8] K. Murphy, B.  van Ginneken, A. M. Schilham, B. J. De Hoop, H. A. Gietema, and M. Prokop, “A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbor classification”. Med. Image Anal., vol. 13, pp. 757-770, 2009.
[9] J. K. Liu, H. Y. Jiang, M. D. Gao, C. G. He, C. G., Y. Wang, P. Wang, and H. Ma, “An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images”. J. Med. Syst., vol. 41, pp. 1-9, 2017.
[10] W. Huang, Y.  Xue, and Y. Wu, “A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning”. Plos one, vol. 14, 2019.
[11] w. Li, P.  Cao, D. Zhao, and J. Wang, “Pulmonary nodule classification with deep convolutional neural networks on computed tomography images”. Comput. Math. Methods Med., vol. 2016, 2016.
[12] A. Ray, “Lung Tumor Segmentation via Fully Convolutional Neural Networks”, 2016.
[13] H. Cao, H. Liu, H., E. Song, G. Ma, X. Xu, R. Jin, and C. C. Hung, “A two-stage convolutional neural networks for lung nodule detection”. IEEE J. Biomed. Health Inform. vol. 24, pp. 2006-2015, 2020.
[14] R. Girshick, J. Donahue, T. Darrell, and J. Malik,  “Region-based convolutional networks for accurate object detection and segmentation”. IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, pp. 142-158, 2015.
[15] R. Girshick, “Fast r-cnn”. In Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.
[16] S. Ren, K.  He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks”. Adv. Neural Inf. Process. Syst., 28, pp. 91-99, 2015.
[17] K. He, G. Gkioxari, P.  Dollr, and R. Girshick,  “Mask r-cnn”. In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017.
[18] S. Kido, Y. Hirano, and N. Hashimoto, “Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN)”. In 2018 International workshop on advanced image technology (IWAIT), pp. 1-4, IEEE, January 2018.
[19] J. Ding, A. Li, Z. Hu, and L. Wang, “Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks”. In International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 559-567, Springer, Cham, September 2017.
[20] W. Zhu, C. Liu, W. Fan, and X. Xie, “Deeplung: Deep 3d dual-path nets for automated pulmonary nodule detection and classification”. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 673-681, IEEE, March 2018.
[21] Z. Xie, “3D region proposal u-net with dense and residual learning for lung nodule detection”. LUNA16, 2017.
[22] Y. Su, D. Li, and X. Chen, “Lung nodule detection based on faster R-CNN framework”, Comput. Methods Programs Biomed., vol. 200, 2021.
[23] H. Xie, D. Yang, N. Sun, Z. Chen, and Y. Zhang, “Automated pulmonary nodule detection in CT images using deep convolutional neural networks”. Pattern Recognit. , vol. 85, pp. 109-119, 2019.
[24] E. R. Capia, A. M. Sousa, and A. X. Falco, “Improving lung nodule detection with learnable non-maximum suppression”, In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI),pp. 1861-1865, IEEE, 2020.
 [25] H. Tang, D. R. Kim, and X. Xie, “ Automated pulmonary nodule detection using 3D deep convolutional neural networks”, In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 523-526, IEEE, April 2018.
[26] L. Cai, T. Long, Y. Dai, and Y. Huang, “Mask R-CNN-based detection and segmentation for pulmonary nodule 3D visualization diagnosis”. IEEE Access, vol. 8, pp. 44400-44409, 2020.
[27] M. Liu, J. Dong, X. Dong, H. Yu, and L. Qi, “ Segmentation of lung nodule in CT images based on mask R-CNN”, In 2018 9th International Conference on Awareness Science and Technology (iCAST), pp. 1-6, IEEE, September 2018.
[28] W. Fan, H. Jiang, L.  Ma, J.  Gao, and H. Yang, “ A modified faster R-CNN method to improve the performance of the pulmonary nodule detection”. In 10th International Conference on Digital Image Processing (ICDIP 2018) (Vol. 10806, p. 108065A). International Society for Optics and Photonics, August 2018.
[29]N. Guo, and Z. Bai, “Multi-scale Pulmonary Nodule Detection by Fusion of Cascade R-CNN and FPN”. In 2021 International Conference on Computer Communication and Artificial Intelligence (CCAI), pp. 15-19, IEEE, May 2021.
[30] J. George, S. Skaria, and V. Varun, “Using YOLO-based deep learning network for real-time detection and localization of lung nodules from low dose CT scans”. In Medical Imaging 2018: Computer-Aided Diagnosis International Society for Optics and Photonics, p. 105751, February 2018.
[31] L. Xinzheng, J.  Wei, L. Gang, and Y. Caoqian, “YOLO V2 Network with Asymmetric Convolution Kernel for Lung Nodule Detection of CT Image”. Chinese Journal of Biomedical Engineering, 2019.
[32] L. Haibo, T. Shanli, S. Shuang, and L. Haoran, “An improved yolov3 algorithm for pulmonary nodule detection”. In 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Vol. 4, pp. 1068-1072, IEEE, June 2021.
[33] W. He, X. Y.  Zhang, F. Yin, and C. L. Liu, “Multi-oriented and multi-lingual scene text detection with direct regression”, IEEE Trans. Image Process., vol. 27, pp. 5406-5419, 2018.
[34] A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx) The Cancer Imaging Archive (TCIA) Public Access, Cancer Imaging Archive Wiki.
[35] J. Barazande, and N. Farzaneh, “WSAMLP: Water Strider Algorithm and Artificial Neural Network-based Activity Detection Method in Smart Homes”. Journal of AI and Data Mining, 2021.