K-Nearest Neighbor Optimization of Silver-Graphene Fiber Optic Sensor for Lung Cancer Detection
Biosensing; Graphene dioxide; K-nearest neighbor; Localized surface plasmons; Machine learning; Refractive index sensor; Silver nanoparticles
At nearly 1.8 million deaths annually, lung cancer is among the world's top causes of mortality. Cancer is curable up to a point, after which recovery is extremely challenging. Preventing cancer requires early cancer detection, which localized surface plasmon resonance (LSPR)-based sensors high sensitivity. The phenomenon known as localized surface plasmon resonance (LSPR) occurs when nanoparticles resonate with light at certain wavelengths, leading to the development of characteristics including quick reaction times, adjustable resonance, high sensitivity, and localized light-matter interaction. Since silver-graphene has qualities that make it perfect for cancer detection, it is selected as the material composition. The silver-graphene sensor is utilized for detecting CL1-5 and A549 cell lines, for which the peak of the extinction coefficients was found to be 2.7169 and 1.8592, with a sensitivity of 107 RIU. The Silver-Graphene LSPR sensor interaction with cell lines generated a novel dataset, for which K-Nearest Neighbor Regression has been chosen due to its adaptability and robustness to outliers and has been used to improve the functionality of the sensor by optimizing sensor design, improving sensor sensitivity, and reducing experimental time. With a prediction rate of 99%, KNN and the Silver-Graphene LSPR sensor are an excellent combination for early lung cancer diagnosis. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Chebbi, Nandan; Mahashabe, Roshan Balaji; Nandi, Somesh; Kumar, Sandeep; Jayanthi, P.N.; Sahana, B.
Smart Innovation, Systems and Technologies;Volume;121 SIST;pp.461-473
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-6254-8_35 https://www.scopus.com/pages/publications/105018909230?origin=resultslist
Chebbi N., Department of ECE, RV College of Engineering, Bengaluru, India; Mahashabe R.B., Department of ECE, RV College of Engineering, Bengaluru, India; Nandi S., Department of AIML, RV College of Engineering, Bengaluru, India; Kumar S., Department of CSE, Christ University, Bengaluru, India; Jayanthi P.N., Department of ECE, RV College of Engineering, Bengaluru, India; Sahana B., Department of ECE, RV College of Engineering, Bengaluru, India