International Conference on Artificial Intelligence and Signal Processing (AISP)

A Deep Learning Driven Quantum Hybrid Approach for Early Breast Cancer Detection

Pallakonda Siri, G. Anitha, Maddukuri Reshma Naga Venkata Durga

Published Nov 22, 2025• 1 citation

Abstract

Breast cancer is also the most diagnosed cancer in women worldwide, and over 2.3 million new cases are annually reported. Early detection is crucial for successful treatment and better survival. In this work, we present a new hybrid model which employs CNN with QML to achieve better classification in breast US images. To address this issue, we work with the publicly available BUSI Dataset from Kaggle that consists of 780 grayscale ultrasound images of benign, malignant, and normal categories, and existing which is widely used by the community and contains normal, benign and malignant samples, and develop a hybrid model based on a classical CNN feature extractor followed by a four-qubit Parametrized Quantum Circuit (PQC), with PennyLane and IBM Q, for prediction and classification of classes after visual and statistical exploratory analysis that evidences an important class imbalance in the data and non-linear feature separability, for which the hybrid architecture performs statistically significantly better. The model achieves accuracy of 95.4%, which is superior to that of the classical CNN baselines. This study demonstrates the potential of QML in medical imaging and early cancer detection

DOI: 10.1109/aisp68263.2025.11396250

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