Rahul Deb Mohalder
M. Raihan
ABSTRACT
One of the most difficult to diagnose and one of the deadliest diseases is lung cancer. A big reason for this is that it takes a long time to identify at an early stage. For treatment, a rapid and precise diagnosis of nodules is very crucial. In order to identify cancer in its early stages, a variety of techniques have been employed. Machine learning approaches were used in this work in order to identify lung cancer nodules. We used machine learning algorithms such as LightGBM, XGBoost, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, and Random Forest to discover anomalous data. We compared all of the approaches. The results of the experiments reveal that LightGBM produces the greatest outcomes with 99.91 percent accuracy, 0.001261 loss and XGBoost outcomes with 99.86 percent accuracy, 0.001446 loss.
- Kamil Belkhayat Abou Omar. 2018. XGBoost and LGBM for Porto Seguro’s Kaggle challenge: a comparison. Preprint Semester Project(2018).Google Scholar
- Abhir Bhandary, G Ananth Prabhu, V Rajinikanth, K Palani Thanaraj, Suresh Chandra Satapathy, David E Robbins, Charles Shasky, Yu-Dong Zhang, João Manuel RS Tavares, and N Sri Madhava Raja. 2020. Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recognition Letters 129 (2020), 271–278.Google ScholarCross Ref
- Subrato Bharati, Prajoy Podder, Rajib Mondal, Atiq Mahmood, and Md Raihan-Al-Masud. 2018. Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer. In International Conference on Intelligent Systems Design and Applications. Springer, 447–457.Google Scholar
- Siddharth Bhatia, Yaash Sinha, and Lavika Goel. 2019. Lung cancer detection: a deep learning approach. In Soft Computing for Problem Solving. Springer, 699–705.Google Scholar
- Andrew A Borkowski, Marilyn M Bui, L Brannon Thomas, Catherine P Wilson, Lauren A DeLand, and Stephen M Mastorides. 2019. Lung and colon cancer histopathological image dataset (lc25000). arXiv preprint arXiv:1912.12142(2019).Google Scholar
- Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, 2015. Xgboost: extreme gradient boosting. R package version 0.4-2 1, 4 (2015), 1–4.Google Scholar
- Raul Victor Medeiros Da Nóbrega, Solon Alves Peixoto, Suane Pires P da Silva, and Pedro Pedrosa Rebouças Filho. 2018. Lung nodule classification via deep transfer learning in CT lung images. In 2018 IEEE 31st international symposium on computer-based medical systems (CBMS). IEEE, 244–249.Google ScholarCross Ref
- David S Ettinger, Wallace Akerley, Gerold Bepler, Matthew G Blum, Andrew Chang, Richard T Cheney, Lucian R Chirieac, Thomas A D’Amico, Todd L Demmy, Apar Kishor P Ganti, 2010. Non–small cell lung cancer. Journal of the national comprehensive cancer network 8, 7 (2010), 740–801.Google ScholarCross Ref
- Özge Günaydin, Melike Günay, and Öznur Şengel. 2019. Comparison of lung cancer detection algorithms. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). IEEE, 1–4.Google Scholar
- Gongde Guo, Hui Wang, David Bell, Yaxin Bi, and Kieran Greer. 2003. KNN model-based approach in classification. In OTM Confederated International Conferences” On the Move to Meaningful Internet Systems”. Springer, 986–996.Google ScholarCross Ref
- Yong Han, Yuan Ma, Zhiyuan Wu, Feng Zhang, Deqiang Zheng, Xiangtong Liu, Lixin Tao, Zhigang Liang, Zhi Yang, Xia Li, 2021. Histologic subtype classification of non-small cell lung cancer using PET/CT images. European journal of nuclear medicine and molecular imaging 48, 2(2021), 350–360.Google Scholar
- Oliver Kramer. 2013. K-nearest neighbors. In Dimensionality reduction with unsupervised nearest neighbors. Springer, 13–23.Google Scholar
- Suren Makaju, PWC Prasad, Abeer Alsadoon, AK Singh, and A Elchouemi. 2018. Lung cancer detection using CT scan images. Procedia Computer Science 125 (2018), 107–114.Google ScholarCross Ref
- Negar Maleki, Yasser Zeinali, and Seyed Taghi Akhavan Niaki. 2021. A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection. Expert Systems with Applications 164 (2021), 113981.Google Scholar
- Sanidhya Mangal, Aanchal Chaurasia, and Ayush Khajanchi. 2020. Convolution Neural Networks for diagnosing colon and lung cancer histopathological images. arXiv preprint arXiv:2009.03878(2020).Google Scholar
- Mehedi Masud, Niloy Sikder, Abdullah-Al Nahid, Anupam Kumar Bairagi, and Mohammed A AlZain. 2021. A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework. Sensors 21, 3 (2021), 748.Google ScholarCross Ref
- Rahul Deb Mohalder, Juliet Polok Sarkar, Khandkar Asif Hossain, Laboni Paul, and M. Raihan. 2021. A Deep Learning Based Approach to Predict Lung Cancer from Histopathological Images. In 2021 International Conference on Electronics, Communications and Information Technology (ICECIT). https://ieeexplore.ieee.org/document/9641341/, 1–4.Google Scholar
- Pankaj Nanglia, Sumit Kumar, Aparna N Mahajan, Paramjit Singh, and Davinder Rathee. 2020. A hybrid algorithm for lung cancer classification using SVM and Neural Networks. ICT Express (2020).Google Scholar
- Mahesh Pal. 2005. Random forest classifier for remote sensing classification. International journal of remote sensing 26, 1 (2005), 217–222.Google ScholarCross Ref
- Irina Rish 2001. An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence, Vol. 3. 41–46.Google Scholar
- Gur Amrit Pal Singh and PK Gupta. 2019. Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Computing and Applications 31, 10 (2019), 6863–6877.Google ScholarDigital Library
- Wenqing Sun, Bin Zheng, and Wei Qian. 2016. Computer aided lung cancer diagnosis with deep learning algorithms. In Medical imaging 2016: computer-aided diagnosis, Vol. 9785. International Society for Optics and Photonics, 97850Z.Google Scholar
- SVM Vishwanathan and M Narasimha Murty. 2002. SSVM: a simple SVM algorithm. In Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No. 02CH37290), Vol. 3. IEEE, 2393–2398.Google ScholarCross Ref
Index Terms
- Efficient Machine Learning Techniques to Predict Lung Cancer
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