Survey on Early Detection of Lung Cancer using Image Processing and Quantum Annealing

DOI

10.2991/978-94-6239-616-6_95How to use a DOI?

Keywords

Lung cancer detection; Medical image analysis; Feature extraction and selection; Feature analysis; Quantum annealing optimization Hybrid quantum-classical approach Machine learning in healthcare

Abstract

Lung Cancer is the deadliest of all diseases worldwide. That’s mostly because it’s often diagnosed too late to be treated. Early diagnosis can be life-saving, but images on CT scans are very difficult to read and conventional methods of diagnosing generally fail. In this work, we develop a novel approach wherein image processing is merged with quantum annealing. It aims to improve the accuracy and efficiency of lung cancer diagnosis. The CT scan images are processed to improve the significant features including texture, shape and intensity using LBP and GLCM based methods. These characteristics can help differentiate benign and malignant nodules. For improving performance, quantum annealing chooses the most relevant features. This reduces the computational burden and maintains accuracy. The filtered data is tagged with machine learning and identifies early-stage cancer. The hybrid model is designed to improve the diagnosis accuracy, decrease the number of false positives as well as make precise predictions in large medical databases. Using quantum-inspired optimization in conjunction with traditional image analysis, the solution demonstrates how modern technology can help care providers to diagnose more accurately and speed diagnoses. In the end, this would lead to better care for patients, early detection and reduced burden of lung cancer.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Suresh
AU  - A. Supha Lakshmi
AU  - S. Jaya Kirthika
AU  - S. Roshini
AU  - Pavithra
PY  - 2026
DA  - 2026/03/31
TI  - Survey on Early Detection of Lung Cancer using Image Processing and Quantum Annealing
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1298
EP  - 1307
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_95
DO  - 10.2991/978-94-6239-616-6_95
ID  - Suresh2026
ER  -