Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method
Abstract
Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)‐based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum‐Classical Convolutional Neural Network (HQC‐CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q‐Means++ Clustering for segmenting the images and a Max‐cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max‐cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.