Advanced Machine Learning Techniques for Precise Lung Cancer Detection from CT Scans
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Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54019-6_30
Reference22 articles.
1. Meraj, T., et al.: Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features (2019). https://doi.org/10.20944/preprints201909.0139.v1
2. Kaucha, D.P., Prasad, P.W.C., Alsadoon, A., Elchouemi, A., Sreedharan, S.: Early detection of lung cancer using SVM classifier in biomedical image processing. In: 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, pp. 3143–3148 (2017). https://doi.org/10.1109/ICPCSI.2017.8392305
3. Sathishkumar, R., Kalaiarasan, K., Prabhakaran, A., Aravind, M.: Detection of lung cancer using SVM classifier and KNN algorithm. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, pp. 1–7 (2019). https://doi.org/10.1109/ICSCAN.2019.8878774
4. Sakumura, Y., et al.: Diagnosis by volatile organic compounds in exhaled breath from lung cancer patients using support vector machine algorithm. Sensors 17(2), 287 (2017). https://doi.org/10.3390/s17020287.[Online].Available:doi:10.3390/s17020287
5. Kancherla, K., Mukkamala, S:. Feature selection for lung cancer detection using SVM based recursive feature elimination method. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2012. Lecture Notes in Computer Science, vol. 7246. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29066-4_15
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