MRI Brain Tumor Detection Using Quantum Mechanics and Neural Networks

Abstract

Quantum computing provides a powerful solution by using quantum parallelism and state rotations to perform the analysis at a far superior rate. Key contributions of this study include: proposing a multi-layer Quantum Neural Networks (QNN) architecture where classical weights are replaced with quantum gates to achieve scalability learning; showing possible use of quantum entanglement in QNN to find features as edges of the tumour; and comparing the QNN model to conventional and semi-quantum models to prove the effectiveness of the quantum model. The methodology involves Magnetic Resonance Imaging (MRI) brain tumour datasets and normalizes and augments the datasets to achieve good results. Qubit utilization is optimized through amplitude and basis encoding, while QNN layers employ rotation and entanglement gates. The model is trained with simulators available in the Qiskit environment and is tested on quantum devices (D-Wave). The QNN obtained an accuracy of 97.2% for biometric data; moreover, it had 96.5% precision and 98.1% recall, and an AUC-ROC score of 0.98 was higher than the classical and hybrid models. However, problems with quantum hardware and data encoding remain understudied and form the basis of possible subsequent experimentation in the healthcare area.