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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2021

19.02.2021

Lung Nodule Classification on Computed Tomography Images Using Fractalnet

verfasst von: Amrita Naik, Damodar Reddy Edla, Venkatanareshbabu Kuppili

Erschienen in: Wireless Personal Communications | Ausgabe 2/2021

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Abstract

Lung Cancer is the most common cancer all over the world and is mostly diagnosed at later stages, thus increasing the risk of death. For early detection of malignant nodules there is a need for automated nodule detection system using imaging modalities like CT scan of lungs. Several machine learning algorithm specially deep learning algorithm have been used to accomplish this task, but without guaranteed accuracy. In the proposed work, we intend to improve the accuracy of pulmonary nodule classification system using fractalnet architecture. Fractalnet is also compared with other deep learning architectures and an elaborative discussion on the same is also mentioned in this paper. We have validated the classification of lung nodules on LUNA dataset and have achieved an accuracy, specificity, sensitivity, area under receiver operating characteristic curve score of 94.7%, 90.41%, 96.68%, 0.98 respectively using fractalnet architecture, which is a substantial improvement over previous works in literature.
Vorheriger Artikel Bandwidth Enhancement of Slotted Rectangular Wideband Microstrip Antenna for the Application of WLAN/WiMAX
Nächster Artikel An Energy-Saving Transmit Method Between Internet of Things Device and Base Station Under Fading Channel

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Literatur
1.
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer Journal for Clinicians, 68, 394–424.CrossRef
2.
Zappa, C., & Mousa, S. A. (2016). Non-small cell lung cancer: current treatment and future advances. Translational Lung Cancer Research, 5(3), 288–300.CrossRef
3.
Wang, W., Chen, G., Chen, H., Dinh, T. T. A., Gao, J., Ooi, B. C., et al. (2016). Deep Learning at Scale and at Ease. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 12(4s), 1–25.
4.
Jin, X., Ma, C., Zhang, Y., & Li, L. (2017). Classification of lung nodules based on convolutional deep belief network. In 10th international symposium on computational intelligence and design (ISCID) Hangzhou (vol. 1, pp. 139–142).
5.
Zhu, W., Liu, C., Fan, W., & Xie, X. (2018). 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).
6.
Ali, I., Hart, G. R., Gunabushanam, G., Liang, Y., Muhammad, W., Nartowt, B., et al. (2018). Lung Nodule detection via deep reinforcement learning. Frontiers in Oncology, 8, 108.CrossRef
7.
Singh, G. A. P., & Gupta, P. K. (2019). Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Computing and Applications, 31, 6863–6877.CrossRef
8.
Pham, T. D., (2017) Complementary features for radiomic analysis of malignant and benign mediastinal lymph nodes. In IEEE international conference on image processing (ICIP) Beijing (pp. 3849–3853).
9.
Pezeshk, A., Hamidian, S., Petrick, N., & Sahiner, B. (2019). 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE Journal of Biomedical and Health Informatics, 23(5), 2080–2090.CrossRef
10.
Zhao, C., Han, J., Jia, Y., & Gou, F. (2019). Lung nodule detection via 3D U-Net and contextual convolutional neural network. In 2018 international conference on networking and network applications (NaNA) (pp. 356–361).
11.
Wei Shen, Mu., Zhou, F. Y., Dongdong, Yu., Dong, Di., Yang, C., Zang, Y., & Tian, J. (2017). Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition, 61, 663–673.CrossRef
12.
Hua, K. L., Hsu, C. H., Hidayati, S. C., Cheng, W. H., & Chen, Y. J. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and Therapy, 8, 2015–2022.
13.
Van, J. G., Bram, S., Arnaud, A. A., Jacobs, C., & Ciompi, F. (2015) Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In SPIE medical imaging (pp. 286–289).
14.
Zhan, J., Xia, Y., & HaoyueZeng, Y. Z. (2018). NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection. Neurocomputing, 317, 159–167.CrossRef
15.
Liu, X., Hou, F., Qin, H., & Hao, A. (2018). Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recognition, 77, 262–275.CrossRef
16.
Jiang, H., Ma, He., Qian, W., Gao, M., & Li, Y. (2018). An Automatic detection system of lung nodule based on multigroup patch-based deep learning network. IEEE Journal of Biomedical and Health Informatics, 22(4), 1227–1237.CrossRef
17.
Sori, W. J., Feng, J., & Liu, S. (2019). Multi-path convolutional neural network for lung cancer detection. Multidimensional Systems and Signal Processing, 30, 1–20.CrossRef
18.
Wangxia Zuo; Fuqiang Zhou; Zuoxin Li; Lin Wang. (2019). Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access, 7, 32510–32521.CrossRef
19.
Ding, J., Li, A., Hu, Z., & Wang, L. (2017). Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, & S. Duchesne (Eds.), Medical image computing and computer assisted intervention: MICCAI 2017: MICCAI 2017: Lecture notes in computer science (Vol. 10435). Cham: Springer. https://​doi.​org/​10.​1007/​978-3-319-66179-7_​64.CrossRef
20.
Fan, L., Xia, Z., Zhang, X., Feng, X. (2017). Lung nodule detection based on 3D convolutional neural networks. In International conference on the frontiers and advances in data science (FADS) (pp. 7–10).
21.
Dou, Qi. (2017). Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Transactions on Biomedical Engineering, 64(7), 1558–1567.CrossRef
22.
Paslawski, M., Krzyzanowski, K., Zlomaniec, J., & Gwizdak, J. (2004). Morphological characteristics of malignant solitary pulmonary nodules. Annales Universitatis Mariae Curie-Sklodowska. Sectio D: Medicina, 59(1), 6–13.
23.
Setio, A. A. A., Traverso, A., De Bel, T., Setio, A. A. A., Traverso, A., de Bel, T., et al. (2017). Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Medical Image Analysis, 42, 1–13.CrossRef
24.
25.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.CrossRef
26.
He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV (pp. 770–778).
27.
Xie, H., Yang, D., Sun, N., Chen, Z., & Zhang, Y. (2019). Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognition, 85, 109–119.CrossRef
28.
Yu, Gu., Xiaoqi, Lu., LidongYang, B. Z., DahuaYu, Y. Z., Gao, L., Liang, Wu., & Zhou, T. (2018). Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Computers in Biology and Medicine, 103, 220–231.CrossRef
29.
30.
Jin, H., Li, Z., Tong, R., & Lin, L. (2018). A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection. Medical Physics, 45(5), 2097–2107.CrossRef
Metadaten
Titel
Lung Nodule Classification on Computed Tomography Images Using Fractalnet
verfasst von
Amrita Naik
Damodar Reddy Edla
Venkatanareshbabu Kuppili
Publikationsdatum
19.02.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-08258-w

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