A Hybrid Quantum Approach to Unsupervised Image Segmentation for Early Breast Cancer Detection

DOI

10.2991/978-94-6239-616-6_61How to use a DOI?

Keywords

Hybrid Quantum Computing; Unsupervised Segmentation; Noisy Intermediate Scale Quantum (NISQ); Hybrid Quantum Optimization; Breast Cancer Imaging; Variational Quantum Circuits (VQC); Quantum Neural Networks (QNN); Gaussian Mixture Models (GMMs); Quantum Unconstrained Binary Optimization (QUBO); Principal Component Analysis (PCA)

Abstract

Breast cancer remains has the major health concern at global level, ranking as one of the most cancer among women worldwide and is the leading cause of cancer-related deaths. In 2022, it has affected 2.3 million women across the globe and lead to more than 670,000 deaths [1]. Faster Early detection is crucial for effective treatment, medication and also can improve patient survival rates [3]. For example, survival rates for localized breast cancer can exceed 90%, whereas advanced stages have significantly lower levels of risk. Traditional breast cancer diagnosis often could rely on the rigorous examination of imaging, such as mammograms and tomographs, making radiologists to identify and categorize abnormality of lesions. This manual process of checking is very time-consuming, costlier and susceptible to human errors and variation because of radiologist’s fatigue. The complexity of breast tissues, varying appearances of masses such as shape, margins, intensity and other subtle detail makes accurate manual differentiation between benign and malignant challenging to find.

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.

Download article (PDF)

TY  - CONF
AU  - R. Suresh
AU  - A. Supha Lakshmi
AU  - G. Bhanuj
AU  - C. Mugundhan Kumar
AU  - K. Raghul
PY  - 2026
DA  - 2026/03/31
TI  - A Hybrid Quantum Approach to Unsupervised Image Segmentation for Early Breast Cancer Detection
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 812
EP  - 825
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_61
DO  - 10.2991/978-94-6239-616-6_61
ID  - Suresh2026
ER  -