Deep Learning Methods for Lung Cancer Nodule Classification: A Survey
DOI:
https://doi.org/10.13052/jmm1550-4646.18213Abstract
Lung cancer is one of the leading causes of cancer related deaths. It is due to the complexity of early detection of nodules. In clinical practice, radiologists find it difficult to determine whether a condition is normal or abnormal by manually analysing CT scan or X-ray images for nodule identification. Currently, various deep learning techniques have been developed to identify lung nodules as benign or malignant, but each technique has its own advantages and drawbacks. This work presents a thorough analysis based on segmentation techniques, Related features-based detection, multi-step detection, automatic detection, and deep convolutional neural network techniques. Performance comparison was conducted on a selected works based on performance measures. A potential research direction for the recognition of lung nodules is given at the end of this study.
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References
The National Lung Screening Trial Research Team. (2011). “Reduced lung-cancer mortality with low-dose computed tomographic screening”. New England Journal of Medicine, 365(5), 395–409.
Rumelhart, D., Hinton, G. & Williams, R. “Learning representations by back-propagating errors”. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0
Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybernetics 36, 193–202 (1980). https://doi.org/10.1007/BF00344251.
Hinton, G. E., & Salakhutdinov, R. R. (2006). “Reducing the dimensionality of data with neural networks”. Science, 313(5786), 504
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. (2014). “Generative Adversarial Nets”. Advances Neural Information Processing Systems, 2672–2680.
Salakhutdinov, R., & Hinton, G. (2012). “An efficient learning procedure for deep Boltzmann machines”. Neural Computation, 24(8), 1967–2006.
Sepp Hochreiter; Jürgen Schmidhuber (1997). “Long short-term memory”. Neural Computation. 9(8): 1735–1780. doi:10.1162/neco.1997.9.8. 1735.
Broomhead, D.S., Lowe, David (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks (Technical report).
Broomhead, D.S., Lowe, David (1988). “Multivariable functional interpolation and adaptive networks”. Complex Systems. 2: 321–355.
Schwenker F., Kestler H.A., Palm G. “Three learning phases for radial-basis-function networks.” Neural networks: the official journal of the International Neural Network Society, vol. 14,4–5 (2001): 439–58. doi:10.1016/s0893-6080(01)00027-2.
Marius, Popescu & Balas, Valentina & Perescu-Popescu, Liliana & Mastorakis, Nikos. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems. 8.
Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982). https://doi.org/10.1007/BF00337288.
Tanzila Saba, “Automated lung nodule detection and classification based on multiple classifiers voting”, Microscopy Research and Technique, vol. 82, no. 9, pp. 1601–1609, 2019.
Atsushi Teramoto and Hiroshi Fujita, “Automated lung nodule detection using positron emission tomography/computed tomography”, In Artificial Intelligence in Decision Support Systems For Diagnosis in Medical Imaging, Springer, Cham, pp. 87–110, 2018, 10.1007/978-3-319-68843-5_4.
Shiwen Shen, Alex AT Bui, Jason Cong and William Hsu, “An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy”, Computers in Biology and Medicine, vol. 57, pp. 139–149, 2015, 10.1016/j.compbiomed.2014.12.008.
Syed Muhammad Naqi, Muhammad Sharif and Ikram Ullah Lali, “A nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection”, Multimedia Tools and Applications, vol. 78, no. 18, pp. 26287–26311, 2019.
Qinghai Zhang and Xiaojing Kong, “Design of automatic lung nodule detection system based on multi-scene deep learning framework”, IEEE Access, vol. 8, pp. 90380–90389, 2020, 10.1109/ACCESS.2020.2993872.
Sunyi Zheng, Jiapan Guo, Xiaonan Cui, Raymond NJ Veldhuis, Matthijs Oudkerk and Peter MA Van Ooijen, “Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection”, IEEE Transactions on Medical Imaging, vol. 39, no. 3, pp. 797–805, 2019.
Wenkai Huang and Lingkai Hu, “Using a noisy U-net for detecting lung nodule candidates”, IEEE Access, vol. 7, pp. 67905–67915, 2019, 10.1109/ACCESS.2019.2918224.
Ling Fu, Jingchen Ma, Yacheng Ren, Youn Seon Han and Jun Zhao, “Automatic detection of lung nodules false positive reduction using convolution neural networks and handcrafted features”, In Medical Imaging Computer-Aided Diagnosis, International Society for Optics and Photonics, vol. 10134, pp. 101340A, 2017, 10.1117/12.2253995.
Wei Shen, Mu Zhou, Feng Yang, Dongdong Yu, Di Dong, Caiyun Yang, Yali Zang and Jie Tian, “Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification”, Pattern Recognition, vol. 61, pp. 663–673, 2017, 10.1016/j.patcog.2016.05.029.
Hongyang Jiang, He Ma, Wei Qian, Mengdi Gao and Yan Li, “An automatic detection system of lung nodule based on multigroup patch-based deep learning network”, IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 4, pp. 1227–1237, 2017.
Wangxia Zuo, Fuqiang Zhou, Zuoxin Li and Lin Wang, “Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection”, IEEE Access, vol. 7, pp. 32510–32521, 2019, 10.1109/ACCESS.2019.2903587.
Tanzila Saba, Ahmed Sameh, Fatima Khan, Shafqat Ali Shad and Muhammad Sharif, “Lung nodule detection based on ensemble of hand crafted and deep features”, Journal of Medical Systems, vol. 43, no. 12, pp. 1–12, 2019.
Talha Meraj, Hafiz Tayyab Rauf, Saliha Zahoor, Arslan Hassan, M. Ikram Ullah Lali, Liaqat Ali, Syed Ahmad Chan Bukhari and Umar Shoaib, “Lung nodules detection using semantic segmentation and classification with optimal features”, Neural Computing and Applications, pp. 1–14, 2019, 10.20944/preprints201909.0139.v1.
Yuyun Ye, Miao Tian, Qiyu Liu and Heng-Ming Tai, “Pulmonary nodule detection using v-net and high-level descriptor based svm classifier”, IEEE Access, vol. 8, pp. 176033–176041, 2020, 10.1109/ACCESS.2020.3026168.
Patrice Monkam, Shouliang Qi, Mingjie Xu, Haoming Li, Fangfang Han, Yueyang Teng and Wei Qian, “Ensemble learning of multiple-view 3D-CNNs model for micro-nodules identification in CT images”, IEEE Access, vol. 7, pp. 5564–5576, 2018, 10.1109/ACCESS.2018.2889350.
Man Tan, Fa Wu, Bei Yang, Jinlian Ma, Dexing Kong, Zengsi Chen and Dan Long, “Pulmonary nodule detection using hybrid two-stage 3D CNNs”, Medical physics, vol. 47, no. 8, pp. 3376–3388, 2020.
Zheng, Guangyuan, Guanghui Han, and Nouman Qadeer Soomro, “An inception module CNN classifiers fusion method on pulmonary nodule diagnosis by signs”, Tsinghua Science and Technology, vol. 25, no. 3, pp. 368–383, 2019.
Lin Lu, Yongqiang Tan, Lawrence H. Schwartz and Binsheng Zhao, “Hybrid detection of lung nodules on CT scan images”, Medical Physics, vol. 42, no. 9, pp. 5042–5054, 2015.
Xiuyuan Xu, Chengdi Wang, Jixiang Guo, Lan Yang, Hongli Bai, Weimin Li and Zhang Yi, “DeepLN a framework for automatic lung nodule detection using multi-resolution CT screening images”, Knowledge-Based Systems, vol. 189, pp. 105128, 2020, 10.1016/j.knosys.2019.105128.
Imdad Ali, Muhammad Muzammil, Ihsan Ul Haq, Amir A. Khaliq and Suheel Abdullah, “Efficient lung nodule classification using transferable texture convolutional neural network”, IEEE Access, vol. 8, pp. 175859–175870, 2020, 10.1109/ACCESS.2020.3026080.
Chung-Feng Jeffrey Kuo, Chang-Chiun Huang, Jing-Jhong Siao, Chia-Wen Hsieh, Vu Quang Huy, Kai-Hsiung Ko and Hsian-He Hsu, “Automatic lung nodule detection system using image processing techniques in computed tomography”, Biomedical Signal Processing and Control, vol. 56, pp. 101659, 2020, 10.1016/j.bspc.2019.101659.
Syed Muhammad Naqi, Muhammad Sharif and Mussarat Yasmin, “Multistage segmentation model and SVM-ensemble for precise lung nodule detection”, International Journal of Computer Assisted Radiology and Surgery, vol. 13, no. 7, pp. 1083–1095, 2018.
Yuanli Feng, Pengyi Hao, Peng Zhang, Xinguo Liu, Fuli Wu and Hongwei Wang, “Supervoxel based weakly-supervised multi-level 3D CNNs for lung nodule detection and segmentation”, Journal of Ambient Intelligence and Humanized Computing, pp. 1–11, 2019, 10.1007/s12652-018-01170-5.
Jibi John and M. G. Mini, “Multilevel thresholding-based segmentation and feature extraction for pulmonary nodule detection”, Procedia Technology, vol. 24, pp. 957–963, 2016, 10.1016/j.protcy.2016.05.209.
Ezhil E Nithila and Kumar S.S., “Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering”, Alexandria Engineering Journal, vol. 55, no. 3, pp. 2583–2588, 2016.
Amal Eisapour Moghaddam, Gholamreza Akbarizadeh and Hooman Kaabi, “Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model”, Signal, Image and Video Processing, vol. 13, no. 3, pp. 457–464, 2019.
He Ren, Lingxiao Zhou, Gang Liu, Xueqing Peng, Weiya Shi, Huilin Xu, Fei Shan and Lei Liu, “An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing”, Quantitative Imaging in Medicine and Surgery, vol. 10, no. 1, pp. 233, 2020.
Joana Rocha, António Cunha and Ana Maria Mendonça, “Conventional filtering versus u-net based models for pulmonary nodule segmentation in ct images”, Journal of Medical Systems, vol. 44, no. 4, pp. 1–8, 2020.
Ji-kui Liu, Hong-yang Jiang, Chen-guang He, Yu Wang, Pu Wang and He Ma, “An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images”, Journal of Medical Systems, vol. 41, no. 2, pp. 30, 2017.
Benita K.J. Veronica, “An effective neural network model for lung nodule detection in CT images with optimal fuzzy model”, Multimedia Tools and Applications, pp. 1–21, 2020, 10.1007/s11042-020-08618-x.
Haichao Cao, Hong Liu, Enmin Song, Chih-Cheng Hung, Guangzhi Ma, Xiangyang Xu, Renchao Jin and Jianguo Lu, “Dual-branch residual network for lung nodule segmentation”, Applied Soft Computing, vol. 86, pp. 105934, 2020,
Erdal Taşcı and Aybars Uğur, “Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs”, Journal of Medical Systems, vol. 39, no. 5, pp. 1–13, 2015,
Furqan Shaukat, Gulistan Raja, Rehan Ashraf, Shehzad Khalid, Mudassar Ahmad and Amjad Ali, “Artificial neural network based classification of lung nodules in CT images using intensity shape and texture features”, Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 10, pp. 4135–4149, 2019.
Syed Muhammad Naqi, Muhammad Sharif and Arfan Jaffar, “Lung nodule detection and classification based on geometric fit in parametric form and deep learning”, Neural Computing and Applications, vol. 32, no. 9, pp. 4629–4647, 2020.
Changmiao Wang, Ahmed Elazab, Jianhuang Wu and Qingmao Hu, “Lung nodule classification using deep feature fusion in chest radiography”, Computerized Medical Imaging and Graphics, vol. 57, pp. 10–18, 2017, 10.1016/j.compmedimag.2016.11.004.
Giovanni Lucca França da Silva, Thales Levi Azevedo Valente, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva and Marcelo Gattass, “Convolutional neural network-based PSO for lung nodule false positive reduction on CT images”, Computer Methods and Programs in Biomedicine, vol. 162, pp. 109–118, 2018, 10.1016/j.cmpb.2018.05.006.
Furqan Shaukat, Gulistan Raja, Ali Gooya and Alejandro F. Frangi, “Fully automatic detection of lung nodules in CT images using a hybrid feature set”, Medical Physics, vol. 44, no. 7, pp. 3615–3629, 2017.
Yutong Xie, Jianpeng Zhang, Yong Xia, Michael Fulham and Yanning Zhang, “Fusing texture shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT”, Information Fusion, vol. 42, pp. 102–110, 2018, 10.1016/j.inffus.2017.10.005.
Jingjing Yuan, Xinglong Liu, Fei Hou, Hong Qin and Aimin Hao, “Hybrid-feature-guided lung nodule type classification on CT images”, Computers & Graphics, vol. 70, pp. 288–299, 2018, 10.1016/j.cag.2017.07.020.
Farahani F. V, Ahmadi A & Zarandi M. H. F, “Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning”, Mathematics and Computers in Simulation, vol. 149, pp. 48–68, 2018, 10.1016/j.matcom.2018.02.001.
Yulei Qin, Hao Zheng, Xiaolin Huang, Jie Yang and Yue-Min Zhu, “Pulmonary nodule segmentation with CT sample synthesis using adversarial networks”, Medical Physics, vol. 46, no. 3, pp. 1218–1229, 2019.
Dhara AK, Mukhopadhyay S, Dutta A, Garg M, Khandelwal N. A, “Combination of shape and texture features for classification of pulmonary nodules in lung CT images”, Journal of Digital Imaging, vol. 29, no. 4, pp. 466–75, 2016.
Xuechen Li, Linlin Shen and Suhuai Luo, “A solitary feature-based lung nodule detection approach for chest X-ray radiographs”, IEEE journal of biomedical and health informatics, vol. 22, no. 2, pp. 516–524, 2017.
Noor Khehrah, Muhammad Shahid Farid, Saira Bilal and Muhammad Hassan Khan, “Lung nodule detection in CT images using statistical and shape-based features”, Journal of Imaging, vol. 6, no. 2, pp. 6, 2020.
Nasrullah N, Sang J , Alam M. S, Mateen M, Cai B & Hu H, “Automated lung nodule detection and classification using deep learning combined with multiple strategies”, Sensors, vol. 19, no. 17, pp. 3722, 2019.
Hongtao Xie, Dongbao Yang, Nannan Sun, Zhineng Chen and Yongdong Zhang, “Automated pulmonary nodule detection in CT images using deep convolutional neural networks”, Pattern Recognition, vol. 85, pp. 109–119, 2019, 10.1016/j.patcog.2018.07.031.
Xuechen Li, Linlin Shen, Xinpeng Xie, Shiyun Huang, Zhien Xie, Xian Hong and Juan Yu, “Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection”, Artificial Intelligence in Medicine, vol. 103, pp. 101744, 2020, 10.1016/j.artmed.2019.101744.
Yu Gu, Xiaoqi Lu, Lidong Yang, Baohua Zhang, Dahua Yu, Ying Zhao, Lixin Gao, Liang Wu and Tao Zhou, “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, vol. 103, pp. 220–231, 2018, 10.1016/j.compbiomed.2018.10.011.
Xia Huang, Wenqing Sun, Tzu-Liang Bill Tseng, Chunqiang Li and Wei Qian, “Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks”, Computerized Medical Imaging and Graphics, vol. 74, pp. 25–36, 2019, 10.1016/j.compmedimag.2019.02.003.
Wei Li, Peng Cao, Dazhe Zhao and Junbo Wang, “Pulmonary nodule classification with deep convolutional neural networks on computed tomography images”, Computational and Mathematical Methods in Medicine, 2016, 10.1155/2016/6215085.
Haichao Cao, Hong Liu, Enmin Song, Guangzhi Ma, Xiangyang Xu, Renchao Jin, Tengying Liu and Chih-Cheng Hung, “A two-stage convolutional neural networks for lung nodule detection”, IEEE Journal of Biomedical And Health Informatics, vol. 24, no. 7, pp. 2006–2015, 2020.
Julio Cesar Mendoza Bobadilla and Helio Pedrini, “Lung nodule classification based on deep convolutional neural networks”, In Iberoamerican Congress on Pattern Recognition, Springer, Cham, pp. 117–124, 2016.
Sunyi Zheng, Ludo J. Cornelissen, Xiaonan Cui, Xueping Jing, Raymond NJ Veldhuis, Matthijs Oudkerk and Peter MA van Ooijen, “Deep convolutional neural networks for multi-planar lung nodule detection improvement in small nodule identification”, Medical Physics, 2020, 10.1002/mp.14648.
Ling Fu, Jingchen Ma, Yizhi Chen, Rasmus Larsson and Jun Zhao, “Automatic detection of lung nodules using 3d deep convolutional neural networks”, Journal of Shanghai Jiaotong University (Science), vol. 24, no. 4, pp. 517–523, 2019.
Qin Wang, Fengyi Shen, Linyao Shen, Jia Huang and Weiguang Sheng, “Lung nodule detection in CT images using a raw patch-based convolutional neural network”, Journal of Digital Imaging, vol. 32, no. 6, pp. 971–979, 2019.
Julio Mendoza and Helio Pedrini, “Detection and classification of lung nodules in chest X-ray images using deep convolutional neural networks”, Computational Intelligence, vol. 36, no. 2, pp. 370–401, 2020.
Sheng Chen, Yaqi Han, Jinqiu Lin, Xiangyu Zhao and Ping Kong, “Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection”, Artificial Intelligence in Medicine, vol. 107, pp. 10188, 2020, 10.1016/j.artmed.2020.101881.
Supriya Suresh and Subaji Mohan, “NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks”, Journal of King Saud University-Computer and Information Sciences, 2019, 10.1016/j.jksuci.2019.11.013.
Ying Su, Dan Li and Xiaodong Chen, “Lung nodule detection based on faster r-cnn framework”, Computer Methods and Programs in Biomedicine, pp. 105866, 2020, 10.1016/j.cmpb.2020.105866.
