International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue III Mar 2023- Available at www.ijraset.com

References

[1] Silva, F., Pereira, T., Morgado, J., Frade, J., Mendes, J., Freitas, C., Negrao, E., De Lima, B.F., Da Silva, M.C., Madureira, A.J. and Ramos, I., 2021. EGFR assessment in lung cancer CT images: analysis of local and holistic regions of interest using deep unsupervised transfer learning. IEEE Access, 9, pp.5866758676.

[2] Das, S. and Majumder, S., 2020, November. Lung cancer detection using deep learning network: A comparative analysis. In 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (pp. 30-35). IEEE.

[3] Loddenkemper, R., Gibson, G.J. and Sibille, Y., 2003. The burden of lung disease in Europe: why a European White Book on lung disease? European Respiratory Journal, 22(6), pp.869-69.

[4] Gibson, G.J., Loddenkemper, R., Lundbäck, B. and Sibille, Y., 2013. Respiratory health and disease in Europe: the new European Lung White Book. European Respiratory Journal, 42(3), pp.559-563.

[5] https://docs.python.org/3/library/tk.html

[6] Paliwal, G. and Kurmi, U., 2021, December. A Comprehensive Analysis of Identifying Lung Cancer via Different Machine Learning Approach. In 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 691-696). IEEE.

[7] Warren, G.W. and Cummings, K.M., 2013. Tobacco and lung cancer: risks, trends, and outcomes in patients with cancer. American Society of Clinical Oncology Educational Book, 33(1), pp.359-364.

[8] https://www.tensorflow.org/api_docs

[9] Wang, S., Chen, A., Yang, L., Cai, L., Xie, Y., Fujimoto, J., Gazdar, A. and Xiao, G., 2018. Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Scientific reports, 8(1), p.10393.

[10] Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T., Franco, I., Mak, R.H. and Aerts, H.J., 2019. Deep learning predicts lung cancer treatment response from serial medical imaginglongitudinal deep learning to track treatment response. Clinical Cancer Research, 25(11), pp.3266-3275.

[11] Zhang, Z., Zohren, S. and Roberts, S., 2019. Deeplob: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing, 67(11), pp.3001-3012.

[12] Niepert, M., Ahmed, M. and Kutzkov, K., 2016, June. Learning convolutional neural networks for graphs. In International conference on machine learning (pp. 2014-2023). PMLR.

[13] Lakshmanaprabu, S.K., Mohanty, S.N., Shankar, K., Arunkumar, N. and Ramirez, G., 2019. Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, pp.374-382.

[14] Zhao, H., Chu, H., Zhang, Y. and Jia, Y., 2020. Improvement of Ancient Shui character recognition model based on convolutional neural network. IEEE Access, 8, pp.33080-33087.

[15] https://www.kaggle.com/datasets

[16] Gysel, P., Pimentel, J., Motamedi, M. and Ghiasi, S., 2018. Ristretto: A framework for empirical study of resource-efficient inference in convolutional neural networks. IEEE transactions on neural networks and learning systems, 29(11), pp.5784-5789.

[17] Tang, S., Yuan, S. and Zhu, Y., 2020. Data preprocessing techniques in convolutional neural network based on fault diagnosis towards rotating machinery. IEEE Access, 8, pp.149487-149496.

[18] Molchanov, P., Tyree, S., Karras, T., Aila, T. and Kautz, J., 2016. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440.

[19] Atzori, M., Cognolato, M. and Müller, H., 2016. Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands. Frontiers in neurorobotics, 10, p.9.

[20] Hu, X., Zeng, Y., Li, Z., Zheng, X., Cai, S. and Xiong, X., 2019. A resources-efficient configurable accelerator for deep convolutional neural networks. IEEE Access, 7, pp.72113-72124.

[21] Raoof, S.S., Jabbar, M.A. and Fathima, S.A., 2020, March. Lung Cancer prediction using machine learning: A comprehensive approach. In 2020 2nd International conference on innovative mechanisms for industry applications (ICIMIA) (pp. 108-115). IEEE.

[22] Wang, X., Chen, H., Gan, C., Lin, H., Dou, Q., Tsougenis, E., Huang, Q., Cai, M. and Heng, P.A., 2019. Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE transactions on cybernetics, 50(9), pp.3950-3962.

[23] Gerber, D.E., Gandhi, L. and Costa, D.B., 2014. Management and future directions in non-small cell lung cancer with known activating mutations. American Society of Clinical Oncology Educational Book, 34(1), pp.e353-e365.