Use of AI Techniques on Photonic Crystal Sensing for the Detection of Tumor
Abstract
Tumors can cause severe problem to human beings. Sometime it can be a cause of death. Earlier there were lack of treatment and technological deficiency, due to which it was unable to detect tumor cells and even unable to offer proper treatment for these diseases. This study aims to use Photonic crystal (PhC) due to their ample choice of structures and litheness to endure with every sphere of influence has been utilized them twenty decade back to now a day and have extremely huge prospects in imminent future also. They have revealed their incidence in the field of imaging, sensing, fabricating industries, automation, medical, mechatronics, computronics, mechanochromic, underwater acoustic detection, pharma industries and nanoimprinting etc. If we are discussing about current and impending applications of PhC then it comprises smart sensing and detection of disunite diseases, anonymous viruses and a range of tumors. Artificial intelligence (AI) is also playing incredibly essential role in analyzing and creating entities equivalent to the change in human behavior. AI tools and techniques are utilizing to create intelligent entities through which it is accomplishing countless feats. The PhC along with the artificial intelligence are utilizing as Optical Neural Network (ONN), Artificial Neural Network (ANN), Cellular Computing, Plasma Technology, Parallel Processing, Image Processing etc. Here in this study designated photonic crystal has been used for the detection of infected cell in human body. Sometimes these infected cells are unable to trace by normal pathological investigations and slowly they take a shape of Tumors. But thanks to Photonics crystal sensors that they have made it true not only for detection but we can say for early detection of such tumors in human body. These early detection and proper investigation is possible only because of AI impacts on photonics crystal. This study focuses on detection and observation of bio molecules for selectivity, sensitivity, reflectivity and concentration. By change in wavelength i.e. from 1.5 μm to 4 μm the refractive index (RI) of tumor cell can be measured which is observed by measuring sensitivity between 11258 nm/RIU to 32358 nm/RIU. Tumors have refractive indices varies between 1.3342 to 1.4251. It is observed that sarcoma level is directly proportional to the RI of tumor. Various AI algorithms like support vector machine (SVM) obtained accuracy as 96%, K- nearest neighbor (KNN) shows as 70%, logistic regression (LR) shows as 88%, random forest (RF) show it as 90%, fuzzy logic (FL) and artificial neural network (ANN) observed accuracy as 93% and 95% respectively.
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References
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3. Gunjan Sahay, Sunil Sharma , Aabhas Mathur , Hemant Dhabhai, Designing of Rectangular Photonic Crystal Fiber to Minimize the Total Dispersion, International Journal of Engineering & Technology (2018) Vol.7,No 4.5 (2018)Pages:268-270.
4. M.F.H. Arif, K. Ahmed, S. Asaduzzaman, M.A.K. Azad, Design and optimization of photonic crystal fiber for liquid sensing applications, Photonic Sens.6 (3) (2016) 279–288.
5. R. Hao et al., “Analysis on photonic crystal fibers with circular air holes in elliptical configuration,” Opt. Fiber Technol. 19(5), 363–368 (2013).
6. Islam, M.R., Hossain, M.A., Talha, K.M.A. et al. A novel hollow core photonic sensor for liquid analyte detection in the terahertz spectrum: design and analysis. Opt Quant Electron 52, 415 (2020). https://doi.org/10.1007/s11082-020-02532-0
7. Sharma S. Tharani L., 2021, AI for photonics and photonics for AI, Material and characteristics integration, IntechOpen, Fiber Optics, ISBN 978-1-83969-627-5
8. Sharma S., Tharani L., Sharma R.K. (2020) Designing a Nonlinear Tri Core Photonic Crystal Fiber for Minimizing Dispersion and Analyzing it in Various Sensing Applications. In: Mathur G., Sharma H., Bundele M., Dey N., Paprzycki M. (eds) International Conference on Artificial Intelligence: Advances and Applications 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1059-5_3
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10. Sharma S., Vyas K., 2013, Novel Design of Honeycomb Photonic Crystal Fiber with nearly zero flattened Chromatic Dispersion, IEEE International Conference on Communication and Signal Processing- (ICCSP'13), ISSN: 2315-4535
11. D. Comoretto, 2010, Introduction To Photonic Crystals: Nanostructured Materials To Manipulate Light Propagation & Harvesting, Trieste 8 – 19 February 2010
12. Benedetto Troia, Antonia Paolicelli, Francesco De Leonardis and Vittorio M. N. Passaro, 2012, IntechOpen, Photonic Crystals for Optical Sensing: A Review, DOI: 10.5772/53897
13. Andrea Chiappini 1 , Lam Thi Ngoc Tran 2 , Pablo Marco Trejo-García 1,3 , Lidia Zur 1 , Anna Lukowiak 4 , Maurizio Ferrari 1 and Giancarlo C. Righini 5,*, 2020, Photonic Crystal Stimuli-Responsive Chromatic Sensors: A Short Review, Micromachines MDPI, 11, 290; doi:10.3390/mi11030290
14. J. M. Fini, “Microstructure fibres for optical sensing in gases and liquids,” Meas. Sci. Technol. 15(6), 1120–1128 (2004).
15. J. Park et al., “Enhancement of chemical sensing capability in a photonic crystal fiber with a hollow high index ring defect at the center,” Opt. Express 19(3), 1921–1929 (2011).
16. S. Olyaee, A. Naraghi, and V. Ahmadi, “High sensitivity evanescent-field gas sensor based on modified photonic crystal fiber for gas condensate and air pollution monitoring,” Optik 125(1), 596–600 (2014).
17. Patel, A., Fisher, J, Nichols, E., et al. (2019). Global, regional, and national burden of brain and other CNS cancer, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology. https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(18)30468-X/fulltext#%20
18. Hollon, T., Pandian, B, Orringer, D. (2019). Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature Medicine. https://www.nature.com/articles/s41591-019-0715-9
19. Lei Zheng and Andrew K. Chan,2001, An Artificial Intelligent Algorithm for Tumor Detection in Screening Mammogram, IEEE Transactions On Medical Imaging, Vol. 20, NO. 7, 0278–0062/01
20. G Rajesh Chandra, Dr. Kolasani Ramchand H Rao, 2016, Tumor Detection In Brain Using Genetic Algorithm, ICCCV 2016 doi: 10.1016/j.procs.2016.03.058
21. Md. Aslam Mollah, Rownok Jahan Usha, Sanjida Tasnim, Kawsar Ahmed, 2020, Detection of cancer afected cell using Sagnac interferometer based photonic crystal fber refractive index sensor, Optical and Quantum Electronics (2020) 52:421 https://doi.org/10.1007/s11082-020-02542-y
22. Laura J. Martin, MD, MPH, 2019, Medically Reviewed Meningioma, December 2019.
23. Zodwa Dlamini, Flavia Zita Francies, Rodney Hull, Rahaba Marima, 2020, Artificial intelligence (AI) and big data in cancer and precision oncology, https://doi.org/10.1016/j.csbj.2020.08.019 2001-0370
24. Walaa M. Nouman, S. E. S. Abd El Ghany, Samira M. Sallam, Abdel Fattah B. Dawood, Arafa H. Aly, 2020, Springer Science+Business Media, LLC, part of Springer Nature 2020
25. Dinish U. Sa, Chit Yaw Fua, Kiat Seng Soha, Ramaswamy Bhuvaneswari, Anil Kumar, Malini Olivoa, 2012, Highly sensitive SERS detection of cancer proteins in low sample volume using hollow core photonic crystal fiber, Biosensors and Bioelectronics, Volume 33, Issue 1
26. Md. Aslam Mollah, Rownok Jahan Usha, Sanjida Tasnim, Kawsar Ahmed, 2020, Detection of cancer afected cell, Springer Science+Business Media, LLC, part of Springer Nature 2020.
27. Erik Ames, Robert, J. Canter, Steven, K. Grossenbacher, Stephanie Mac, Mingyi Chen, Rachel, C. Smith, Takeshi Hagino, Jessica Perez-Cunningham, Gail, D. Sckisel, Shiro Urayama, Arta M. Monjazeb, Ruben C. Fragoso, Thomas J. Sayers, William J. Murphy, The Journal of Immunology October 15, 2015, 195 (8) 4010-4019; DOI: 10.4049/jimmunol.1500447
28. Jain A., Sharma R.K., Agarwal V., Sharma S. (2020) A New Design of Equiangular Circular Cum Elliptical Honeycomb Photonic Crystal Fiber. In: Ranganathan G., Chen J., Rocha Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 89. Springer, Singapore. https://doi.org/10.1007/978-981-15-0146-3_6
29. H. Ademgil, Highly sensitive octagonal photonic crystal fiber based sensor, Optik-Int. J. Light Electron Opt. 125 (20) (2014) 6274–6278
30. Sharma S., Sharma R.K., Gupta R., Dash P. (2020) Design and Analysis of Elliptical Core Spiral Silica Photonic Crystal Fiber with Improved Optical Characteristics. In: Kumar A., Mozar S. (eds) ICCCE 2019. Lecture Notes in Electrical Engineering, vol 570. Springer, Singapore. https://doi.org/10.1007/978-981-13-8715-9_9
31. V. Kaur and S. Singh, “Performance analysis of multichannel surface plasmon resonance sensor with dual coating of conducting metal oxide,” J. Nanophotonics 12(1), 016012 (2018).
32. N. N. Gopal and M. Karnan. Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques. In IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pages 1–4, Dec 2010.
33. H. Najadat, Y. Jaffal, O. Darwish, and N. Yasser. A classifier to detect abnormality in CT brain images. In The 2011 IAENG International Conference on Data Mining and Applications, pages 374–377, Mar 2011.
34. M. F. Othman and M. A. M. Basri. Probabilistic neural network for brain tumor classification. In Second International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pages 136–138, Jan 2011.
35. D. F. Specht. Probabilistic neural networks. Neural Networks, 3(1):109–118, 1990.
36. Piccinotti D., MacDonald K.F., Gregory S.A.,, Youngs I. and Zheludev N.I. ( 2020) Artificial intelligence for photonics and photonic materials, IOP Publishing Ltd. Rep. Prog. Phys. 84 012401
37. Wang, J. J., Xu, Y. Z., Mazzarello, R., Wuttig, M., & Zhang, W. (2017). A Review on Disorder-Driven Metal-Insulator Transition in Crystalline Vacancy-Rich GeSbTe Phase-Change Materials. Materials (Basel, Switzerland), 10(8), 862. https://doi.org/10.3390/ma10080862
38. Soler M, Estevez M.C., Rubio M.C., Astua A., and Lechuga L.M., (2020) How Nano photonic Label-Free Biosensors Can Contribute to Rapid and Massive Diagnostics of Respiratory Virus Infections: COVID-19 Case A CS Sensors, 5 (9), 2663-2678 doi: 10.1021/acssensors.0c01180.
39. V Goda K., Jalali B., Lei C., Situ G., and Westbrook P., ( 2020) AI boosts photonics and vice versa, APL Photon. 5, 070401, doi: 10.1063/5.0017902.
40. Roy, K., Jaiswal, A. & Panda, P., (2019) Towards spike-based machine intelligence with Neur-omorphic computing. Nature 575, 607–617. https://doi.org/10.1038/s41586-019-1677-2.
41. Zeng J., Khanolkar A., Xu P., Colburn S., Deshmukh S., Myers J., Frantz J., Pop E., Hendrickson J., Doylend J., Boechler N., and Majumdar A., (2018) GST-on-silicon hybrid nanophotonic integrated circuits: a non-volatile quasi-continuously reprogrammable platform," Opt. Mater. Express 8, 1551-1561
42. Peng Chen, Mathieu N. Grisolia, Hong Jian Zhao, Otto E. González-Vázquez, L. Bellaiche, Manuel Bibes, Bang-Gui Liu, and Jorge Íñiguez, (2018) Energetics of oxygen-octahedra rotations in perovskite oxides from first principles, Phys. Rev. B 97, 024113
43. Wei, J.; Yi, L.; Giacoumidis, E.; Cheng, Q.; Tao Lau, A.P., (2020) Special Issue on “Optics for AI and AI for Optics, Appl. Sci. 10, no. 9: 3262. https://doi.org/10.3390/app10093262
44. Soler M., Scholtz A., Zeto R., and Armani A.M., (2020) Engineering photonics solutions for COVID-19, APL Photonics 5, 090901, https://doi.org/10.1063/5.0021270
45. Taha, B.A.; Al Mashhadany, Y.; Hafiz Mokhtar, M.H.; Dzulkefly Bin Zan, M.S.; Arsad, N., (2020) An Analysis Review of Detection Corona virus Disease 2019 (COVID-19) Based on Biosensor Application, Sensors, 20, 6764. https://doi.org/10.3390/s20236764
46. Sharma, M. (2012). Artificial Neural Network Fuzzy Inference System (ANFIS) For Brain Tumor Detection. arXiv preprint arXiv:1212.0059.
47. Singh, B. K., Verma, K., & Thoke, A. (2015). Adaptive Gradient Descent Backpropagation for Classification of Breast Tumors in Ultrasound Imaging. Procedia Computer Science, 46, 1601-1609.
48. Singh, S., Saini, S., & Singh, M. (2012). Cancer detection using adaptive neural network. International Journal of Advancements in Research & Technology, 1(4), 93-97.
49. Sivakumar, S., & Chandrasekar, C. (2013). Lung nodule detection using fuzzy clustering and support vector machines. International Journal of Engineering and Technology (IJET), 5(1), 179-185.
50. . Tariq, A., Akram, M. U., & Javed, M. Y. (2013). Lung Nodule Detection in CT images using neuro fuzzy classifier. Paper presented at the Computational Intelligence in Medical Imaging (CIMI), 2013 IEEE Fourth International Workshop.
51. Sapra, P., Singh, R., & Khurana, S. (2013). Brain Tumor Detection Using Neural Network. International Journal of Science and Modern Engineering (IJISME) ISSN, 2319-6386.
52. Nalbalwar, R., Majhi, U., Patil, R., & Gonge, S. Detection of Brain Tumor by using ANN. image, 2(3), 7.
53. Rolnick D, Veit A, Belongie S, Shavit N. Deep learning is robust to massive label noise. ar Xiv:1705.10694 [cs.LG]; 2017. arxiv.org/abs/1705.10694. Accessed May 28, 2018
Published
2022-04-29
How to Cite
[1]
S. Sharma and L. Tharani, “Use of AI Techniques on Photonic Crystal Sensing for the Detection of Tumor”, j.electron.electromedical.eng.med.inform, vol. 4, no. 2, pp. 62-69, Apr. 2022.
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Electronics
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