Streamlining Cancer Diagnosis and Prognosis System using Hybrid CNN-NPR: Deep Learning Approaches
Keywords:
CNN, Deep Learning, CNN-NPR, Cancer diagnosis, Prognosis systemAbstract
In the context of medical science, advancements in technical infrastructure related to computer and life sciences have enabled the utilization of computational methods for medical diagnosis. As the number of cancer cases continues to rise rapidly, the existing diagnostic system is becoming out dated, necessitating the development of modern, productive, and optimized strategies. Accurately predicting the type of cancer is crucial for the diagnosis and treatment of the disease. Knowledge of cancer genes can significantly assist in comprehending, diagnosing, and identifying the different types of cancer. In this study paper, the identification and prediction of cancer type are achieved through the utilization of hybrid CNN-NPR several researchers have proposed different Convolutional Neural Network (CNN) models to date. Every model focused on a specific group of parameters that were utilized to imitate the gene pattern.
Downloads
References
Y. Wang, F. S. Makedon, J. C. Ford, and J. Pearlman, “HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data,” Bioinformatics, vol. 21, no. 8, pp. 1530–1537, 2005.
T. Thakur, I. Batra, M. Luthra et al., “Gene expression-assisted cancer prediction techniques,” Journal of Healthcare Engineering, vol. 2021, Article ID 4242646, 9 pages, 2021.
S. Kaur and G. Kaur, “Threat and vulnerability analysis of cloud platform: a user perspective,” in 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 533–539, New Delhi, India, 2021.
K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction,” Computational and Structural Biotechnology Journal, vol. 13, pp. 8–17, 2015.
Y. Wang, I. V. Tetko, M. A. Hall et al., “Gene selection from microarray data for cancer classification--a machine learning approach,” Computational Biology and Chemistry, vol. 29, no. 1, pp. 37–46, 2005.
G. Jindal and G. Kaur, “A comprehensive overview of quality enhancement approach-based biometric fusion system using artificial intelligence techniques,” Communication and Intelligent Systems, vol. 204, pp. 81–98, 2021.
S. Chaudhury, N. Shelke, K. Sau, B. Prasanalakshmi, and M. Shabaz, “A novel approach to classifying breast cancer histopathology biopsy images using bilateral knowledge distillation and label smoothing regularization,” Computational and Mathematical Methods in Medicine, vol. 2021, Article ID 4019358, 11 pages, 2021.
S.-B. Cho and H.-H. Won, “Cancer classification using ensemble of neural networks with multiple significant gene subsets,” Applied Intelligence, vol. 26, no. 3, pp. 243–250, 2007.
Y. Hu, K. Ashenayi, R. Veltri, G. O'Dowd, G. Miller, and H. R. Bonner, “A comparison of neural network and fuzzy c-means methods in bladder cancer cell classification,” in Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), pp. 3461–3466, Orlando, FL, USA, 1994.
P. H. Levine, K. Ajmera, B. O’Neill, V. Venkatesh, P. Garcia-Gonzalez, and H. J. Hoffman, “Demographic factors related to young age at diagnosis of chronic myeloid leukemia in India,” Clinical Epidemiology and Global Health, vol. 4, no. 4, pp. 188–192, 2016.
A. Elfiky, M. J. Pany, R. B. Parikh, and Z. Obermeyer, “Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy,” JAMA Network Open, vol. 1, no. 3, article e180926, 2018.
S. L. Bangare, G. Pradeepini, and S. T. Patil, “Implementation for brain tumor detection and three dimensional visualization model development for reconstruction,” ARPN Journal of Engineering and Applied Sciences (ARPN JEAS), vol. 13, no. 2, pp. 467–473, 2018.
K. A. Tran, O. Kondrashova, A. Bradley, E. D. Williams, J. V. Pearson, and N. Waddell, “Deep learning in cancer diagnosis, prognosis and treatment selection,” Genome Medicine, vol. 13, no. 1, 2021.
Y. Tang, Y.-Q. Zhang, Z. Huang, H. Xiaohua, and Y. Zhao, “Recursive fuzzy granulation for gene subsets extraction and cancer classification,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 6, pp. 723–730, 2008.
S. Akanksha and K. Parminder, “Optimized liver tumor detection and segmentation using neural network,” International Journal of Recent Technologyand Engineering(IJRTE), vol. 2, no. 5, pp. 7–10, 2013.
H.-H. Won and S.-B. Cho, “Paired neural network with negatively correlated features for cancer classification in DNA gene expression profiles,” in Proceedings of the International Joint Conference on Neural Networks, 2003, pp. 1708–1713, Portland, OR, USA, 2003.
R. Xu, X. Cai, C. Donald, and I. I. Wunsch, “Gene expression data for DLBCL cancer survival prediction with a combination of machine learning technologies,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 894–897, Shanghai, China, 2005.
V. Bevilacqua, G. Mastronardi, F. Menolascina, P. Pannarale, and A. Pedone, “A Novel Multi-Objective Genetic Algorithm Approach to Artificial Neural Network Topology Optimisation: The Breast Cancer Classification Problem,” in The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 1958–1965, Vancouver, BC, Canada, 2006.
L. Ziaei, A. R. Mehri, and M. Salehi, “Application of artificial neural networks in cancer classification and diagnosis prediction of a subtype of lymphoma based on gene expression profile,” Journal of Research in Medical Sciences, vol. 11, no. 1, pp. 13–17, 2006.
H. Takahashi, Y. Murase, T. Kobayashi, and H. Honda, “New cancer diagnosis modeling using boosting and projective adaptive resonance theory with improved reliable index,” Biochemical Engineering Journal, vol. 33, no. 2, pp. 100–109, 2007.
D. M. Joshi, N. K. Rana, and V. M. Misra, “Classification of brain cancer using artificial neural network,” in 2010 2nd International Conference on Electronic Computer Technology, pp. 112–116, Kuala Lumpur, Malaysia, 2010.
P. Rajeswari and G. S. Reena, “Human liver cancer classification using microarray gene expression data,” Proceedings of International Journal of Computer Applications, vol. 34, no. 6, pp. 25–37, 2011.
B. Sahu and D. Mishra, “A novel feature selection algorithm using particle swarm optimization for cancer microarray data,” Procedia Engineering, vol. 38, pp. 27–31, 2012.
S. Swathi, G. Anjan Babu, R. Sendhilkumar, and S. N. Bhukya, “Performance of ART1 network in the detection of breast cancer,” Proceedings of International conference on Computer design and Engineering (ICCDE 2012), vol. 49, pp. 100–105, 2012.
M.S Kumar, "Prediction of Heart Attack from Medical Records Using Big Data Mining", International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(4s), pp. 90–99.
M.S Kumar, "Nature-Inspired Optimisation-Based Regression Based Regression to Study the Scope of Professional Growth in Small and Medium Enterprises, International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(4s), pp. 100–108.
Natarajan, V.A., "Improving QoS in Wireless Sensor Network routing using Machine Learning Techniques", Proceedings of the 1st IEEE International Conference on Networking and Communications 2023, ICNWC 2023, 2023.
M.S Kumar, "Chronic Kidney Disease Prediction Using Machine Learning", Journal of Advances in Information Technology, 2023, 14(2), pp. 384–391.
D Ganesh, "Deep Convolution Neural Network Based solution for Detecting Plant Diseases", Journal of Pharmaceutical Negative Results, 2022, 13, pp. 464–471
D Ganesh, "Implementation of Novel Machine Learning Methods for Analysis and Detection of Fake Reviews in Social Media", 2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023 - Proceedings, 2023, pp. 243–250
Ganesh, D., Kumar, T. P., & Kumar, M. S. (2021). Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks. IET Communications, 15(2), 245-256.
Sushama et.al, "Impact of COVID-19 pandemic and the diagnosis of the virus in the human body",World Journal of Engineeringthis link is disabled, 2022, 19(5), pp. 652–657
Anantha Natarajan, V., Kumar, M. S., & Tamizhazhagan, V. (2020). Forecasting of Wind Power using LSTM Recurrent Neural Network. Journal of Green Engineering, 10.
Sushama et.al, Automated extraction of non-functional requirements from text files: A supervised learning approach", Handbook of Intelligent Computing and Optimization for Sustainable Development, 2022, pp. 149–170D.
A. Omondiagbe, S. Veeramani, and A. S. Sidhu, “Machine learning classification techniques for breast cancer diagnosis,” IOP Conference Series: Materials Science and Engineering, vol. 495, article 012033, 2019.
S Girinath,"Deep Learning-based Segmentation and Computer Vision-based Ultrasound Imagery Techniques", 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT),DOI: 10.1109/ICECCT56650.2023.10179723, ISBN:978-1-6654-9361-1, 2023.
Sushama, C.," Global Identification Passport: A Unique Cloud based Passport Model", Proceedings - 5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023, 2023, pp. 801–805
M. S. Kumar, "AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network", Quantum-Safe Cryptography Algorithms and Approaches: Impacts of Quantum Computing on Cybersecur, 2023, pp. 37–47.
M. S. Kumar, "Power aware virtual machine placement in IaaS cloud using discrete firefly algorithm", Applied Nanoscience (Switzerland)this link is disabled, 2023, 13(3), pp. 2003–2011.
Natarajan, V.A., "Improving QoS in Wireless Sensor Network routing using Machine Learning Techniques", Proceedings of the 1st IEEE International Conference on Networking and Communications 2023, ICNWC 2023, 2023T. O. Nielsen, R. B. West, S. C. Linn et al., “Molecular characterisation of soft tissue tumours: a gene expression study,” Lancet, vol. 359, no. 9314, pp. 1301–1307, 2002.
K. Jyothi Prakash. "Internet of things: IETF protocols, algorithms and applications." Int. J. Innov. Technol. Explor. Eng 8.11 (2019): 2853-2857.
Sangamithra, B. "A memetic algorithm for multi objective vehicle routing problem with time windows." 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE). IEEE, 2017.
P. S. Nelson, N. Clegg, H. Arnold et al., “The program of androgen-responsive genes in neoplastic prostate epithelium,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 18, pp. 11890–11895, 2002.
Y. Guo, X. Shang, and Z. Li, “Identification of cancer subtypes by integrating multiple types of transcriptomics data with deep learning in breast cancer,” Neurocomputing, vol. 324, pp. 20–30, 2019.
S. Chatterjee, A. Iyer, S. Avva, A. Kollara, and M. Sankarasubbu, “Convolutional neural networks in classifying cancer through DNA methylation,” 2018, https://arxiv.org/abs/1807.09617.
Z. Si, H. Yu, and Z. Ma, “Learning deep features for DNA methylation data analysis,” IEEE Access, vol. 4, pp. 2732–2737, 2016.
Y. C. Chiu, H. H. Chen, T. Zhang et al., “Predicting drug response of tumors from integrated genomic profiles by deep neural networks,” BMC Medical Genomics, vol. 12, Supplement 1, p. 18, 2019.
P. Luo, Y. Ding, X. Lei, and F. X. Wu, “deepDriver: predicting cancer driver genes based on somatic mutations using deep convolutional neural networks,” Frontiers in Genetics, vol. 10, p. 13, 2019.
Y. C. Chiu, H. I. H. Chen, A. Gorthi et al., “Deep learning of pharmacogenomics resources: moving towards precision oncology,” Briefings in Bioinformatics, vol. 21, no. 6, pp. 2066–2083, 2019.
S. Depuru, K. Santhi, K. Amala, M. Sakthivel, S. Sivanantham and V. Akshaya, "Deep Learning-based Malware Classification Methodology of Comprehensive Study," 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2023, pp. 322-328, doi: 10.1109/ICSCDS56580.2023.10105027.
S. Depuru, S. Sivanantham, K. Amala, V. Akshaya, M. Sakthivel and P. S. Kusuma, "Empirical Study of Human Facial Emotion Recognition: A Deep Learning," 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2023, pp. 1-6, doi: 10.1109/ICCCI56745.2023.10128492.
Sivakumar Depuru , Anjana Nandam , P.A. Ramesh , M. Saktivel , K. Amala , Sivanantham, “Human Emotion Recognition System Using Deep Learning Technique”, Journal of Pharmaceutical Negative Results, vol. 13, no. 4, pp. 1031–1035, Nov. 2022.
K. Pujitha , Kattamanchi Prem Krishna , K. Amala , Annavarapu Yasaswini , Sivakumar Depuru , Kopparam Runvika, “Development of Secured Online Parking Spaces”, Journal of Pharmaceutical Negative Results, vol. 13, no. 4, pp. 1010–1013, Nov. 2022.
Kulkarni, L. . (2022). High Resolution Palmprint Recognition System Using Multiple Features. Research Journal of Computer Systems and Engineering, 3(1), 07–13. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/35
G, M. ., Deshmukh, P. ., N. L., U. K. ., Macedo, V. D. J. ., K B, V. ., N, A. P. ., & Tiwari, A. K. . (2023). Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 118–126. https://doi.org/10.17762/ijritcc.v11i3s.6172
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.