Quantum Convolutional Neural Network for Breast Cancer Detection through Mammograms

• Grant number: 24/00117-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: March 01, 2024
• End date: February 28, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: João Paulo Papa
• Grantee: Yasmin Rodrigues Sobrinho
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

The research investigates the convergence between convolutional neural networks (CNNs) and quantum computing (QC) as an innovative approach to breast cancer detection, marking notable advancements in the application of artificial intelligence in medicine. The fusion of these promising technologies results in quantum convolutional neural networks (QCNNs), which aim to enhance classical predictive and diagnostic capabilities by leveraging the intrinsic advantages of quantum mechanics. By incorporating QC into the architecture of CNNs, the possibility of exploring phenomena such as entanglement and superposition arises, promoting a substantial improvement in computational efficiency. This approach aims to overcome traditional challenges, with a particular focus on the early and precise detection of breast cancer. The synergy between deep learning (DL), the engine behind CNNs, and QC offers a transformative perspective for precision medicine. Highly specialized algorithms can analyze complex patterns in medical images, identifying anomalies in data more rapidly and accurately. This joint scientific development represents a significant step towards personalizing diagnosis and treatment, contributing to optimizing clinical resources and, consequently, improving outcomes for patients affected by this condition. This study explores how QCNNs can be used as an innovative tool for detecting breast cancer. It will investigate how quantum technology, artificial intelligence, and medicine can work together to advance specialized technologies. By combining the benefits of these technologies, the research aims to improve diagnostics' effectiveness and efficiency while promoting a more personalized approach to patient care. This collaboration holds the potential to transform the present and pave the way for a new era where quantum machine learning (QML) and medicine merge even more profoundly. This convergence offers innovative prospects for the next generation of medical advancements, where complex medical challenges can be addressed with novel solutions resulting from the powerful combination of these fields.