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Journals / The Journal of Cognitive Systems / 2017 / Volume: 2 Issue: 2

DEEP CONVOLUTIONAL NEURAL NETWORKS TO DETECT LUNG CANCER STAGE

H. Selcuk Nogay(MESLEK YÜKSEKOKULU)0000-0001-9105-508X
Pages
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Publication Date
2017-12-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, a classification of 99.8% accuracy was performed in order to stage lung cancer with the proposed DCNN model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Regardless of the type of cancer, the treatment process starts after the staging process.
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Key Findings & Data:In the study, 200 data for each stage of lung cancer and 1800 MR images for 9 stages in total were used.
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Conclusion & Impact:As a result, a classification of 99.8% accuracy was performed in order to stage lung cancer with the proposed DCNN model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Deep learning · Convolutional neural network · Lung cancer

Abstract

Regardless of the type of cancer, the treatment process starts after the staging process. The treatment method to be applied to the cancer patient depends on the stage of the disease. Therefore, all studies on the staging of cancer types have a big precaution. In this study, deep convolutional neural networks (DCNN) were used for staging of lung cancer. The TNM classification system is considered for staging. According to TNM, there are nine tumor stages in lung cancer. In the study, 200 data for each stage of lung cancer and 1800 MR images for 9 stages in total were used. As a result, a classification of 99.8% accuracy was performed in order to stage lung cancer with the proposed DCNN model.

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Cite This Article

Nogay, H. S. (2017). DEEP CONVOLUTIONAL NEURAL NETWORKS TO DETECT LUNG CANCER STAGE. The Journal of Cognitive Systems, Volume: 2 Issue: 2. https://ajindex.com/articles/1552025

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References (21)

  1. Nastaran Emaminejad, Wei Qian, Yubao Guan, Maxine Tan, Yuchen Qiu, Hong Liu, and Bin Zheng, Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients, IEEE Transactions on Biomedical Engineering, v. 63, n. 5, may 2016.
  2. Fukuoka M, Masuda N, Negoro S, et al. CODE chemotherapy with or without granulocyte colony stimulating factor in small cell lung cancer. Br J Cancer 1997;75:306-309.
  3. Klasa RJ, Murray N, Coldman AJ. Dose-intensity meta-analysis of chemotherapy regimens in small-cell carcinoma of the lung. J ClinOncol 1991;9:499-508.
  4. David Mattes, David R. Haynor, Hubert Vesselle, Thomas K. Lewellen, and William Eubank, PET-CT Image Registration in the Chest Using Free-form Deformations, IEEE Transactions on Medical Imaging, v. 22, n.1, Jan. 2003.
  5. U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in Biology and Medicine, (2017) 1–9.
  6. M.E. Paoletti, J.M. Haut, J. Plaza, A. Plaza, A new deep convolutional neural network for fast hyperspectral image classification, ISPRS Journal of Photogrammetry and Remote Sensing, 31, May, 2017, 1-28.
  7. Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha, Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images, EbioMedicine, 2017, 1-12.
  8. Yushan Zheng, Zhiguo Jiang, Fengying Xie, Haopeng Zhang, Yibing Ma, Huaqiang Shi, Yu Zhao Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification, Pattern Recognition 71 (2017) 14–25,
  9. Schild SE, Bonner JA, Shanahan TG, et al. Long-term results of a phase III trial comparing once-daily radiotherapy with twice-daily radiotherapy in limited-stage small-cell lung cancer. Int J Radiat Oncol Biol Phys 2004;59:943-951.
  10. Anjali Kulkarni, Anagha Panditrao, Classification of Lung Cancer Stages on CT Scan Images Using Image Processing, 2014 IEEE International Conference on Advanced Connnunication Control and Computing Teclmologies (lCACCCT).
  11. Prionjit Sarker, Md. Maruf Hossain Shuvo, Zakir Hossain, and Sabbir Hasan, Segmentation and Classification of Lung Tumor from 3D CT Image using K-means Clustering Algorithm, Proceedings of the 2017 4th International Conference on Advances in Electrical Engineering (ICAEE) 8-30 September, Dhaka, Bangladesh.
  12. Vallières, M. et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer, Sci Rep, 7, 10117 (2017). doi: 10.1038/s41598-017-10371-5.
  13. http://www.cancerimagingarchive.net/, date of access: 10 Jan 2017,
  14. http://www.tiplopedi.com/TNM_evreleme_sistemi, date of access: 13 Jan 2017.
  15. https://www.mathworks.com/help/nnet/examples/create-simple-deep-learning-network-for-classification.html, date of access: 13 Jan 2017.
  16. Chris Pearce, Convolutional Neural Networks and the Analysis of Cancer Imagery, Stanford University, 2017.
  17. Fabio A. Spanhol, Luiz S. Oliveira, Caroline Petitjean, and Laurent Heutte, Breast Cancer Histopathological Image Classification using Convolutional Neural Networks, Saint Etienne du Rouvray, France (2017).
  18. Harry Pratt, Frans Coenen, Deborah M Broadbent, Simon P Harding, Yalin Zheng, Convolutional Neural Networks for Diabetic Retinopathy, International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016, 6-8 July 2016, Loughborough, UK.
  19. Yushan Zheng, Zhiguo Jiang, Fengying Xie, Haopeng Zhang, Yibing Ma, Huaqiang Shi, Yu Zhao Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification, Pattern Recognition 71 (2017) 14–25,
  20. http://www.cancerimagingarchive.net/, date of access: 10 Jan 2017,
  21. https://www.mathworks.com/help/nnet/examples/create-simple-deep-learning-network-for-classification.html, date of access: 13 Jan 2017.