A Deep Learning Driven Volatile Organic Compounds Analysis for Lung Cancer Detection Using HC-PCF and Convolutional Neural Networks - Upadhyay, Jaitesh; Bagga, Dr. Shobi; Mathur, Dr. Dhirendra (Photonics Letters of Poland, 2025)
A Volatile Organic Compounds (VOC) detection system for lung cancer diagnosis through deep learning (DL) technology is implemented in a special Hollow-Core Photonic Crystal Fibre (HC-PCF) sensor platform. COMSOL Multiphysics is used to simulate the HC-PCF. A hexagonal lattice structure of silica material with 1 μm pitch dimensions and 0.5 μm air hole diameters allow for exceptional light guidance and VOC interaction when detecting exhaled breath components. The sensor achieves a remarkable refractive index sensitivity of 920 nm/RIU for detecting cancerous and non-cancerous VOC profiles. The refractive index measurements of lung cancer-related VOC samples fell within 1.380 to 1.392, while VOC samples from healthy patients ranged from 1.350 to 1.360. Sensor spectral response data processing relied on a Convolutional Neural Network (CNN) model that was trained to distinguish different VOC signature patterns. When applied to a dataset of 1,200 breath samples consisting of 600 cancer-positive and 600 healthy specimens, the CNN architecture reached a 96.3% overall classification accuracy combined with 94.7% sensitivity and 97.8% specificity. Full Text: PDF References S. Sharma, L. Tharani, "Photonic Crystal Fiber Sensor Design for Enhanced Tumor Detection: Structural Optimization and Sensitivity Analysis", Photonics Lett. Poland 16(2), 25 (2024). CrossRef A. Yasli, "Cancer Detection with Surface Plasmon Resonance-Based Photonic Crystal Fiber Biosensor", Plasmonics 16, 1605 (2021). CrossRef S. Sharma, S. Das, C.S. Shieh et al. "Design and Numerical Analysis of a Gold-Coated Photonic Crystal Fiber Sensor for Metabolic Disorder Detection with Deep Learning Assistance", Plasmonics (2025). CrossRef N. Ayyanar, G.T. Raja, M. Sharma, D.S. Kumar, "Photonic Crystal Fiber-Based Refractive Index Sensor for Early Detection of Cancer", IEEE Sensors Journal 18(17), 7093 (2018). CrossRef S. Sharma, L. Tharani, "Photonics for AI and AI for photonics integration : Materials and characteristics", J. Information and Optimization Sciences 45(3), 805 (2024). CrossRef M. Babińska, A. Władziński, "Enhanced Sensitivity of Absorption Spectroscopy Glucose Detection by Machine Learning", Photonics Lett. Poland 17(1), 16 (2025). CrossRef M. Babińska, A. Władziński, T. Talaśka, M. Szczerska, "Machine Learning Enhanced Optical Fiber Sensor For Detection Of Glucose Low Concentration In Samples Mimicking Tissue", Photonics Lett. Poland 17(1), 20 (2025). CrossRef