Lung Cancer Detection Using AI and Different Techniques of Machine Learning
Keywords:
machine learning, organization, lung cancer, artificial intelligenceAbstract
One of the most prevalent malignancies, lung cancer accounts for about 225,000 cases, 150,000 deaths, and $12 billion in annual health care costs in the United States. Additionally, it is one of the most lethal malignancies overall, according to a revolutionary group. The survival rate is lower in underdeveloped nations, and 17 November of Americans who are diagnosed with carcinoma survive for 5 years after diagnosis. The date of a blight indicates how much it has spread. Stages one and two discuss tumours that are restricted to the lungs, whereas later stages discuss cancers that have spread to other organs. Currently used diagnostic methods include imaging tests like CT scans and biopsies. Early cancer diagnosis (discovery in the early stages) considerably improves survival prospects. However, since fewer effects are recorded in certain areas, it is more challenging to detect early stages of cancer. As a bi-classification flaw, it will be our responsibility to check for carcinoma in patient CT lung scans that have or do not have early stage cancer. To create the ideal classifier, we choose to employ PC vision and deep learning techniques, particularly the convergence of second and third neural networks. The elimination of white Gaussian scan image noise created by Gabor filter operation and the twin tree difficult moving ridge rework (DTCWT) rework technology to phase the respiratory organ is a major issue in each location. In this article, numerous quality features are discovered using the SVM algorithm.
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Shankar K, Lakshmanaprabu SK, Khanna A, Tanwar S, Rodrigues JJ, Roy NR (2019) Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier. Comput Electr Eng 77:230–243.
Naqi SM, Sharif M, Lali IU (2019) A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection. Multimed Tools Appl 78(18):26287–26311.
Zhang R, Cheng C, Zhao X, Li X (2019) Multiscale mask R-CNN–based lung tumor detection using PET Imaging. Mol Imaging 18:1536012119863531.
Joon P, Bajaj SB, Jatain A (2019) Segmentation and detection oflung cancer using image processing and clustering techniques. In:Saeed K, Chaki N, Pati B, Bakshi S, Mohapatra DP (eds) Progress in advanced computing and intelligent engineering. Springer, Singapore, pp 13–23
Vijh, Surbhi; Gaurav, Prashant; Pandey, Hari Mohan (2020). Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection. Neural Computing and Applications, (), –. doi:10.1007/s00521-020-05362-z
Abdullah, D. M., & Ahmed, N. S. (2021). A Review of most Recent Lung Cancer Detection Techniques using Machine Learning. International Journal of Science and Business, 5(3), 159-173.
Agrawal, A., Gans, J., and Goldfarb, A. (2019). Artificial Intelligence, Automation, and Work. In The Economics of Artificial Intelligence. https://doi.org/10.7208/chicago/9780226613
003.0008
Al-shamasneh, A. R. M., & Obaidellah, U. H. B. (2017). Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study. European Scientific Journal. https://doi.org/10.
/esj.2016.v13n3p342
Al Daoud, E., & Al-Daoud, E. (2010). Cancer Diagnosis Using Modified Fuzzy Network. Universal Journal of Computer Science and Engineering Technology.
Alikhani, N., Ferguson, R. D., Novosyadlyy, R., Gallagher, E. J., Scheinman, E. J., Yakar, S., & Leroith, D. (2013). Mammary tumor growth and pulmonary metastasis are enhanced in a hyperlipidemic mouse model. Oncogene. https://doi.org/10.1038/onc.2012.113
Arslan, M. T., Arslan, D., & Haznedar, B. (2018). TRAINING ANFIS SYSTEM WITH GENETIC ALGORITHM FOR DIAGNOSIS OF PROSTATE CANCER. E-Journal of New World Sciences Academy. https://doi.org/10.12739/nwsa.2018.13.4.2a0
Bao, L., Zhang, Z. L., Tian, Z. Q., Zhang, L., Liu, C., Lin, Y., Qi, B., & Pang, D. W. (2011). Electrochemical tuning of luminescent carbon nanodots: From preparation to luminescence mechanism. Advanced Materials. https://doi.org/10.1002/adma.201102866
Cascio, D., Magro, R., Fauci, F., Iacomi, M., & Raso, G. (2012). Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2012.09.002
Chaudhary, A., & Singh, S. S. (2012). Lung cancer detection on CT images by using image processing. Proceedings: Turing 100 - International Conference on Computing Sciences, ICCS 2012. https://doi.org/10.1109/ICCS.2012.43
Chavoshi, S., Azmin Sulaiman, W. N., Saghafian, B., Bin Sulaiman, M. N., & Manaf, L. A. (2013). Regionalization by fuzzy expert system based approach optimized by genetic algorithm. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2013.
033
Church, T. R., Black, W. C., Aberle, D. R., Berg, C. D., Clingan, K. L., Duan, F., Fagerstrom, R. M., Gareen, I. F., Gierada, D. S., Jones, G. C., Mahon, I., Marcus, P. M., Sicks, J. R. D., Jain, A., & Baum, S. (2013). Results of initial low-dose computed tomographic screening for lung cancer. New England Journal of Medicine. https://doi.org/10.1056/NEJMoa1209120
Daliri, M. R. (2012). A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines. Journal of Medical Systems. https://doi.org/
1007/s10916-011-9806-y
Dharmarajan, A., & Velmurugan, T. (2015). Lung cancer data analysis by k-means and farthest first clustering algorithms. Indian Journal of Science and Technology. https://doi.org/10.17485/
ijst/2015/v8i15/73329
Dutta, P., Chakraborty, D., & Roy, A. R. (2005). A single-period inventory model with fuzzy random variable demand. Mathematical and Computer Modelling. https://doi.org/10.1016/j.
mcm.2004.08.007
Farahani, F. V., Zarandi, M. H. F., & Ahmadi, A. (2015). Fuzzy rule based expert system for diagnosis of lung cancer. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS. https://doi.org/10.1109/NAFIPS-WConSC.
7284206
Ganesan, D. N., Venkatesh, D. K., Rama, D. M. A., & Palani, A. M. (2010). Application of Neural Networks in Diagnosing Cancer Disease using Demographic Data. International Journal of Computer Applications. https://doi.org/10.5120/476-783
Garcea, S. C., Wang, Y., & Withers, P. J. (2018). X-ray computed tomography of polymer composites. In Composites Science and Technology. https://doi.org/10.1016/j.compscitech.2017.10.023
Goni, I., U. Ngene, C., I., M., Nata’ala, A., & J. Calvin, S. (2018). Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy. Asian Journal of Research in Computer Science. https://doi.org/10.9734/ajrcos/2018/v2i124763
Hamdan, H., & Garibaldi, J. M. (2010). An investigation of the effect of input representation in anfis modelling of breast cancer survival. ICFC 2010 ICNC 2010 - Proceedings of the International Conference on Fuzzy Computation and International Conference on Neural Computation.
Hashemi, A., Pilevar, A. H., & Rafeh, R. (2013). Mass Detection in Lung CT Images Using Region Growing Segmentation and Decision Making Based on Fuzzy Inference System and Artificial Neural Network. International Journal of Image, Graphics and Signal Processing. https://doi.org/10.5815/ijigsp.2013.06.03
Hecke, R., & Ramchandran, K. (2017). The sample complexity of online one-class collaborative filtering. 34th International Conference on Machine Learning, ICML 2017.
Jang, J. S. R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics. https://doi.org/10.1109/21.256541
Javad Hamidzadeh, Reza Javadzadeh, A. N. (2015). Fuzzy Rule Based Diagnostic System For Detecting The Lung Cancer Disease. Journal of Renewable Natural Resources Bhutan.
Jung, K. W., Won, Y. J., Oh, C. M., Kong, H. J., Cho, H., Lee, D. H., & Lee, K. H. (2015). Prediction of cancer incidence and mortality in Korea, 2015. Cancer Research and Treatment. https://doi.org/10.4143/crt.2015.066
Karaboga, D., & Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. In Artificial Intelligence Review. https://doi.org/10.
/s10462-017-9610-2
Karaboǧa, D., & Kaya, E. (2017). Training ANFIS by using the artificial bee colony algorithm. Turkish Journal of Electrical Engineering and Computer Sciences. https://doi.org/10.3906/elk-1601-240
Karbab, E. M. B., Debbabi, M., & Mouheb, D. (2016). Fingerprinting android packaging: Generating DNAs for malware detection. DFRWS 2016 USA - Proceedings of the 16th Annual USA Digital Forensics Research Conference. https://doi.org/10.
/j.diin.2016.04.013
Khalil, A. M., Li, S. G., Lin, Y., Li, H. X., & Ma, S. G. (2020). A new expert system in prediction of lung cancer disease based on fuzzy soft sets. Soft Computing. https://doi.org/10.1007/s00500-020-04787-x
Khatri, P., Singh, S. R., Belani, N. K., Yeong, Y. L., Lohan, R., Lim, Y. W., & Teo, W. Z. (2020). YouTube as source of information on 2019 novel coronavirus outbreak: a cross sectional study of English and Mandarin content. Travel Medicine and Infectious Disease. https://doi.org/10.1016/j.tmaid.2020.101636
Krishan, J., Anand, S., Mayya, Y. S., & Seth, J. R. (2020). Evolution dynamics of fractal aggregates in continuously stirred tank aerosol reactors using generalized condensation exponents. Aerosol Science and Technology. https://doi.org/10.1080/0278
2020.1731412
Kumar, N. (2010). Neurologic Presentations of Nutritional Deficiencies. In Neurologic Clinics. https://doi.org/10.1016/j.
ncl.2009.09.006
Kuruvilla, J., & Gunavathi, K. (2015). Lung cancer classification using fuzzy logic for CT images. International Journal of Medical Engineering and Informatics.
Pandey, J.K., Ahamad, S., Veeraiah, V., Adil, N., Dhabliya, D., Koujalagi, A., Gupta, A. Impact of call drop ratio over 5G network (2023) Innovative Smart Materials Used in Wireless Communication Technology, pp. 201-224.
Soundararajan, R., Stanislaus, P.M., Ramasamy, S.G., Dhabliya, D., Deshpande, V., Sehar, S., Bavirisetti, D.P. Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks (2023) Energies, 16 (5), art. no. 2285,
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