Research progress of auxiliary diagnosis classification algorithm for lung tumor imaging_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CUI Dongmei ,  WEI Guohui , ZHANG Xike ,  MA Zhiqing

College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, 250355, P. R. China;

Corresponding?author：

WEI Guohui, Email: bmie530@163.com; MA Zhiqing, Email: mazhq126@163.com

Keywords：

Lung tumor classification; deep learning; radiomics; multiple data fusion; model fusion; artificial intelligence; review

DOI：

10.7507/1007-4848.202201049

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The classification of lung tumor with the help of computer-aided diagnosis system is very important for the early diagnosis and treatment of malignant lung tumors. At present, the main research direction of lung tumor classification is the model fusion technology based on deep learning, which classifies the multiple fusion data of lung tumor with the help of radiomics. This paper summarizes the commonly used research algorithms for lung tumor classification, introduces concepts and technologies of machine learning, radiomics, deep learning and multiple data fusion, points out the existing problems and difficulties in the field of lung tumor classification, and looks forward to the development prospect and future research direction of lung tumor classification.

Citation： CUI Dongmei, WEI Guohui, ZHANG Xike, MA Zhiqing. Research progress of auxiliary diagnosis classification algorithm for lung tumor imaging. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(7): 892-898. doi: 10.7507/1007-4848.202201049 Copy

1.	巨娟, 林檬, 曾祥飛, 等. 基于CT影像智能分析診斷早期肺癌的最新研究進展. 中國胸心血管外科臨床雜志, 2021, 28(3): 354-357.
2.	Kozuka T, Matsukubo Y, Kadoba T, et al. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol, 2020, 38(11): 1052-1061.
3.	魏一娟, 潘寧, 陳巖, 等. 深度學習輔助診斷系統在胸片的應用研究: 氣胸及肺結節檢測. 臨床放射學雜志, 2021, 40(2): 252-257.
4.	尹柯, 張久權, 伍建林, 等. 對比卷積神經網絡分類模型與放射科醫師鑒別浸潤性肺腺癌的效能. 中國醫學影像技術, 2021, 37(9): 1338-1342.
5.	Bhat S, Shashikala R, Kumar S, et al. Convolutional neural network approach for the classification and recognition of lung nodules. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020: 1310-1314.
6.	Nadkarni NS, Borkar S. Detection of lung cancer in CT images using image processing. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019: 863-866.
7.	Li M, Ma XJ, Chen C, et al. Research on the auxiliary classification and diagnosis of lung cancer subtypes based on histopathological images. IEEE Access, 2021, 9: 53687-53707.
8.	Gopi K, Selvakumar J. Lung tumor area recognition and classification using EK-mean clustering and SVM. International Conference on Nextgen Electronic Technologies: Silicon to Software, 2017: 97-100.
9.	Easwaran U, Kandasamy Y, Chellappan R, et al. Impact of biomaterials in lung tumor classification and segmentation using machine learning healthcare. Mater Today Proc, 2021, 43(5): 3100-3104.
10.	Patel V, Shah S, Trivedi H, et al. An analysis of lung tumor classification using SVM and ANN with GLCM features. In: Singh P, Pawłowski W, Tanwar S, et al. eds. Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Singapore: Springer, 2020.
11.	Potghan S, Rajamenakshi R, Bhise A. Multi-layer perceptron based lung tumor classification. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018.
12.	何校棟, 邢海群, 王瞳, 等. 基于Adaboost算法的多特征融合肺部PET-CT圖像的腫瘤分類方法. 中國醫學裝備, 2017, 14(8): 5-10.
13.	Sarker P, Shuvo M, Hossain Z, et al. Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm. 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, 2017.
14.	Agrawal VL, Dudul SV. Conventional neural network approach for the diagnosis of lung tumor. 2020 International Conference on Computational Performance Evaluation (ComPE), 2020: 543-547.
15.	Azzawi H, Hou J, Alnnni R, et al. SBC: A new strategy for multiclass lung cancer classification based on tumour structural information and microarray data. 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018: 68-73.
16.	Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
17.	楊培偉, 周余紅, 邢崗, 等. 卷積神經網絡在生物醫學圖像上的應用進展. 計算機工程與應用, 2021, 57(7): 44-58.
18.	Wang YW, Chen CC, Wang TC, et al. Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning. Comput Biol Med, 2022, 141: 105185.
19.	Mukherjee S, Bohra SU. Lung cancer disease diagnosis using machine learning approach. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020: 207-211.
20.	Abdul W. An automatic lung cancer detection and classification (ALCDC) system using convolutional neural network. 2020 13th International Conference on Developments in eSystems Engineering (DeSE), 2020: 443-446.
21.	Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
22.	Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
23.	Kasinathan G, Jayakumar S, Gandomi AH, et al. Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier. Exp Sys Appl, 2019, 134: 112-119.
24.	Agarwal A, Patni K, Rajeswari D. Lung cancer detection and classification based on Alexnet CNN. 2021 6th International Conference on Communication and Electronics Systems (ICCES), 2021: 1390-1397.
25.	Ss A, Sm B. NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks. Comp Inform Sci, 2022, 35(4): 1706-1717.
26.	Mohanapriya N, Kalaavathi B, Kuamr TS. Lung tumor classification and detection from CT scan images using deep convolutional neural networks (DCNN). 2019 International Conference on Computational Intelligence and Knowledge Economy, 2019: 800-805.
27.	李斌, 李科宇, 湯渝玲, 等. 基于深度學習的肺癌計算機輔助診斷. 當代醫學, 2021, 27(9): 89-93.
28.	Gong J, Liu J, Hao W, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol, 2020, 30(4): 1847-1855.
29.	Xu X, Hou R, Zhao W, et al. A weak supervision-based framework for automatic lung cancer classification on whole slide image. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020.
30.	史張, 劉崎. 影像組學技術方法的研究及挑戰. 放射學實踐, 2018, 33(6): 633-636.
31.	Kotrotsou A, Zinn PO, Colen RR. Radiomics in brain tumors: An emerging technique for characterization of tumor environment. Magn Reson Imaging Clin N Am, 2016, 24(4): 719-729.
32.	Wang J, Liu X, Dong D, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 1272-1275.
33.	余燁, 吳華偉. 影像組學在肺癌中的應用進展. 國際醫學放射學雜志, 2018, 41(6): 646-649.
34.	陳震東. 基于影像組學的肺腫瘤良惡性分類及早期肺腺癌淋巴結轉移預測模型研究. 浙江師范大學, 2018.
35.	周天綺, 朱超挺, 石峰. 影像組學在肺腫瘤良惡性分類預測中的應用研究. 中國醫療器械雜志, 2020, 44(2): 113-117.
36.	黃志成, 葉釘利, 胡喬治, 鄭君, 趙瑞坤. 基于CT影像組學模型鑒別診斷小細胞肺癌與非小細胞肺癌. 中國介入影像與治療學, 2021, 18(8): 474-478.
37.	石鎮維, 劉再毅. 影像組學研究的困境和出路. 中華放射學雜志, 2022, 56(1): 9-11.
38.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
39.	李雪, 周金治, 莫春梅, 等. 基于特征融合的U-Net肺自動分割方法. 中國醫學物理學雜志, 2021, 38(6): 704-712.
40.	王生生, 王琪. 融合LBP和小波矩特征的肺癌圖像精細分類. 東北師大學報(自然科學版), 2017, 49(2): 57-63.
41.	Luo Z, Brubaker M A, Brudno M. Size and texture-based classification of lung tumors with 3D CNNs. Applications of Computer Vision. IEEE, 2017.
42.	鄭德重. 基于多模態數據融合的肺部腫瘤智能分析技術研究. 中國科學院大學(中國科學院上海技術物理研究所), 2021.
43.	Li L, Lu W, Tan S. Variational PET/CT tumor co-segmentation integrated with PET restoration. IEEE Trans Radiat Plasma Med Sci, 2020, 4(1): 37-49.
44.	張飛飛, 周濤, 陸惠玲, 等. 基于集成VPRS-RUGGA支持向量機的多模態肺部腫瘤計算機輔助診斷模型. 生物醫學工程研究, 2019, 38(1): 48-53.
45.	吳翠穎, 周濤, 陸惠玲, 等. 基于集成SVM的肺部腫瘤PET/CT三模態計算機輔助診斷方法. 生物醫學工程研究, 2017, 36(3): 207-212.
46.	梁蒙蒙. 基于卷積神經網絡的多模態醫學圖像分類研究. 寧夏醫科大學, 2019.
47.	武志遠, 馬圓, 唐浩, 等. 基于深度卷積神經網絡方法構建肺部多模態圖像分類診斷模型. 中國衛生統計, 2019, 36(6): 806-808, 813.
48.	王蕊芳. 基于卷積神經網絡的多模態醫學圖像融合方法研究. 中北大學, 2021.
49.	王媛媛, 周濤, 陸惠玲, 等. 基于集成卷積神經網絡的肺部腫瘤計算機輔助診斷模型. 生物醫學工程學雜志, 2017, 34(4): 543-551.
50.	鄔雪濤, 林嵐, 王婧璇. 基于CT圖像的肺實質分割技術研究進展. 智慧健康, 2019, 5(20): 87-89.
51.	石邈, 續力云, 潘鑫福, 等. 三維重建技術在肺腺癌新分類標準診斷中的價值. 中國胸心血管外科臨床雜志, 2021, 28(3): 278-282.
52.	孫翎馬. 肺部CT影像智能分析及輔助診斷關鍵技術研究. 電子科技大學, 2021.
53.	張紫程. 基于肺癌PET/CT影像的診斷模型研究. 蘭州大學, 2019.
54.	任海玲, 周濤, 霍兵強. 基于集成DE-NRS的肺部腫瘤影像組學計算機輔助診斷模型. 計算機應用與軟件, 2020, 37(5): 156-163, 204.
55.	Hussein S, Kandel P, Bolan CW, et al. Lung and pancreatic tumor characterization in the deep learning era: Novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging, 2019, 38(8): 1777-1787.
56.	Naik A, Edla DR. Lung tumor classification using CNN- and GLCM-based features. 2021.
57.	霍兵強, 周濤, 陸惠玲, 等. 基于NRC和多模態殘差神經網絡的肺部腫瘤良惡性分類. 山東大學學報(工學版), 2020, 50(6): 59-67, 75.
58.	劉銳, 何先波. 基于深度學習的肺部醫學圖像分析研究進展. 川北醫學院學報, 2019, 34(2): 316-320.
59.	韓光輝, 劉峽壁, 鄭光遠. 肺部CT圖像病變區域檢測方法. 自動化學報, 2017, 43(12): 2071-2090.

1. 巨娟, 林檬, 曾祥飛, 等. 基于CT影像智能分析診斷早期肺癌的最新研究進展. 中國胸心血管外科臨床雜志, 2021, 28(3): 354-357.
2. Kozuka T, Matsukubo Y, Kadoba T, et al. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol, 2020, 38(11): 1052-1061.
3. 魏一娟, 潘寧, 陳巖, 等. 深度學習輔助診斷系統在胸片的應用研究: 氣胸及肺結節檢測. 臨床放射學雜志, 2021, 40(2): 252-257.
4. 尹柯, 張久權, 伍建林, 等. 對比卷積神經網絡分類模型與放射科醫師鑒別浸潤性肺腺癌的效能. 中國醫學影像技術, 2021, 37(9): 1338-1342.
5. Bhat S, Shashikala R, Kumar S, et al. Convolutional neural network approach for the classification and recognition of lung nodules. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020: 1310-1314.
6. Nadkarni NS, Borkar S. Detection of lung cancer in CT images using image processing. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019: 863-866.
7. Li M, Ma XJ, Chen C, et al. Research on the auxiliary classification and diagnosis of lung cancer subtypes based on histopathological images. IEEE Access, 2021, 9: 53687-53707.
8. Gopi K, Selvakumar J. Lung tumor area recognition and classification using EK-mean clustering and SVM. International Conference on Nextgen Electronic Technologies: Silicon to Software, 2017: 97-100.
9. Easwaran U, Kandasamy Y, Chellappan R, et al. Impact of biomaterials in lung tumor classification and segmentation using machine learning healthcare. Mater Today Proc, 2021, 43(5): 3100-3104.
10. Patel V, Shah S, Trivedi H, et al. An analysis of lung tumor classification using SVM and ANN with GLCM features. In: Singh P, Pawłowski W, Tanwar S, et al. eds. Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Singapore: Springer, 2020.
11. Potghan S, Rajamenakshi R, Bhise A. Multi-layer perceptron based lung tumor classification. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018.
12. 何校棟, 邢海群, 王瞳, 等. 基于Adaboost算法的多特征融合肺部PET-CT圖像的腫瘤分類方法. 中國醫學裝備, 2017, 14(8): 5-10.
13. Sarker P, Shuvo M, Hossain Z, et al. Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm. 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, 2017.
14. Agrawal VL, Dudul SV. Conventional neural network approach for the diagnosis of lung tumor. 2020 International Conference on Computational Performance Evaluation (ComPE), 2020: 543-547.
15. Azzawi H, Hou J, Alnnni R, et al. SBC: A new strategy for multiclass lung cancer classification based on tumour structural information and microarray data. 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018: 68-73.
16. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
17. 楊培偉, 周余紅, 邢崗, 等. 卷積神經網絡在生物醫學圖像上的應用進展. 計算機工程與應用, 2021, 57(7): 44-58.
18. Wang YW, Chen CC, Wang TC, et al. Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning. Comput Biol Med, 2022, 141: 105185.
19. Mukherjee S, Bohra SU. Lung cancer disease diagnosis using machine learning approach. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020: 207-211.
20. Abdul W. An automatic lung cancer detection and classification (ALCDC) system using convolutional neural network. 2020 13th International Conference on Developments in eSystems Engineering (DeSE), 2020: 443-446.
21. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
22. Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med, 2018, 24(10): 1559-1567.
23. Kasinathan G, Jayakumar S, Gandomi AH, et al. Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier. Exp Sys Appl, 2019, 134: 112-119.
24. Agarwal A, Patni K, Rajeswari D. Lung cancer detection and classification based on Alexnet CNN. 2021 6th International Conference on Communication and Electronics Systems (ICCES), 2021: 1390-1397.
25. Ss A, Sm B. NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks. Comp Inform Sci, 2022, 35(4): 1706-1717.
26. Mohanapriya N, Kalaavathi B, Kuamr TS. Lung tumor classification and detection from CT scan images using deep convolutional neural networks (DCNN). 2019 International Conference on Computational Intelligence and Knowledge Economy, 2019: 800-805.
27. 李斌, 李科宇, 湯渝玲, 等. 基于深度學習的肺癌計算機輔助診斷. 當代醫學, 2021, 27(9): 89-93.
28. Gong J, Liu J, Hao W, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol, 2020, 30(4): 1847-1855.
29. Xu X, Hou R, Zhao W, et al. A weak supervision-based framework for automatic lung cancer classification on whole slide image. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020.
30. 史張, 劉崎. 影像組學技術方法的研究及挑戰. 放射學實踐, 2018, 33(6): 633-636.
31. Kotrotsou A, Zinn PO, Colen RR. Radiomics in brain tumors: An emerging technique for characterization of tumor environment. Magn Reson Imaging Clin N Am, 2016, 24(4): 719-729.
32. Wang J, Liu X, Dong D, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 1272-1275.
33. 余燁, 吳華偉. 影像組學在肺癌中的應用進展. 國際醫學放射學雜志, 2018, 41(6): 646-649.
34. 陳震東. 基于影像組學的肺腫瘤良惡性分類及早期肺腺癌淋巴結轉移預測模型研究. 浙江師范大學, 2018.
35. 周天綺, 朱超挺, 石峰. 影像組學在肺腫瘤良惡性分類預測中的應用研究. 中國醫療器械雜志, 2020, 44(2): 113-117.
36. 黃志成, 葉釘利, 胡喬治, 鄭君, 趙瑞坤. 基于CT影像組學模型鑒別診斷小細胞肺癌與非小細胞肺癌. 中國介入影像與治療學, 2021, 18(8): 474-478.
37. 石鎮維, 劉再毅. 影像組學研究的困境和出路. 中華放射學雜志, 2022, 56(1): 9-11.
38. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
39. 李雪, 周金治, 莫春梅, 等. 基于特征融合的U-Net肺自動分割方法. 中國醫學物理學雜志, 2021, 38(6): 704-712.
40. 王生生, 王琪. 融合LBP和小波矩特征的肺癌圖像精細分類. 東北師大學報(自然科學版), 2017, 49(2): 57-63.
41. Luo Z, Brubaker M A, Brudno M. Size and texture-based classification of lung tumors with 3D CNNs. Applications of Computer Vision. IEEE, 2017.
42. 鄭德重. 基于多模態數據融合的肺部腫瘤智能分析技術研究. 中國科學院大學(中國科學院上海技術物理研究所), 2021.
43. Li L, Lu W, Tan S. Variational PET/CT tumor co-segmentation integrated with PET restoration. IEEE Trans Radiat Plasma Med Sci, 2020, 4(1): 37-49.
44. 張飛飛, 周濤, 陸惠玲, 等. 基于集成VPRS-RUGGA支持向量機的多模態肺部腫瘤計算機輔助診斷模型. 生物醫學工程研究, 2019, 38(1): 48-53.
45. 吳翠穎, 周濤, 陸惠玲, 等. 基于集成SVM的肺部腫瘤PET/CT三模態計算機輔助診斷方法. 生物醫學工程研究, 2017, 36(3): 207-212.
46. 梁蒙蒙. 基于卷積神經網絡的多模態醫學圖像分類研究. 寧夏醫科大學, 2019.
47. 武志遠, 馬圓, 唐浩, 等. 基于深度卷積神經網絡方法構建肺部多模態圖像分類診斷模型. 中國衛生統計, 2019, 36(6): 806-808, 813.
48. 王蕊芳. 基于卷積神經網絡的多模態醫學圖像融合方法研究. 中北大學, 2021.
49. 王媛媛, 周濤, 陸惠玲, 等. 基于集成卷積神經網絡的肺部腫瘤計算機輔助診斷模型. 生物醫學工程學雜志, 2017, 34(4): 543-551.
50. 鄔雪濤, 林嵐, 王婧璇. 基于CT圖像的肺實質分割技術研究進展. 智慧健康, 2019, 5(20): 87-89.
51. 石邈, 續力云, 潘鑫福, 等. 三維重建技術在肺腺癌新分類標準診斷中的價值. 中國胸心血管外科臨床雜志, 2021, 28(3): 278-282.
52. 孫翎馬. 肺部CT影像智能分析及輔助診斷關鍵技術研究. 電子科技大學, 2021.
53. 張紫程. 基于肺癌PET/CT影像的診斷模型研究. 蘭州大學, 2019.
54. 任海玲, 周濤, 霍兵強. 基于集成DE-NRS的肺部腫瘤影像組學計算機輔助診斷模型. 計算機應用與軟件, 2020, 37(5): 156-163, 204.
55. Hussein S, Kandel P, Bolan CW, et al. Lung and pancreatic tumor characterization in the deep learning era: Novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging, 2019, 38(8): 1777-1787.
56. Naik A, Edla DR. Lung tumor classification using CNN- and GLCM-based features. 2021.
57. 霍兵強, 周濤, 陸惠玲, 等. 基于NRC和多模態殘差神經網絡的肺部腫瘤良惡性分類. 山東大學學報(工學版), 2020, 50(6): 59-67, 75.
58. 劉銳, 何先波. 基于深度學習的肺部醫學圖像分析研究進展. 川北醫學院學報, 2019, 34(2): 316-320.
59. 韓光輝, 劉峽壁, 鄭光遠. 肺部CT圖像病變區域檢測方法. 自動化學報, 2017, 43(12): 2071-2090.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress of auxiliary diagnosis classification algorithm for lung tumor imaging

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content