Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks
- URL: http://arxiv.org/abs/2401.15804v2
- Date: Tue, 30 Jan 2024 21:23:39 GMT
- Title: Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks
- Authors: Muhammad Al-Zafar Khan, Nouhaila Innan, Abdullah Al Omar Galib,
Mohamed Bennai
- Abstract summary: This research details a high-precision design and execution of a QCNN model specifically tailored to identify and classify brain cancer images.
Our proposed QCNN architecture and algorithm have achieved an exceptional classification accuracy of 99.67%, demonstrating the model's potential as a powerful tool for clinical applications.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Integrating Quantum Convolutional Neural Networks (QCNNs) into medical
diagnostics represents a transformative advancement in the classification of
brain tumors. This research details a high-precision design and execution of a
QCNN model specifically tailored to identify and classify brain cancer images.
Our proposed QCNN architecture and algorithm have achieved an exceptional
classification accuracy of 99.67%, demonstrating the model's potential as a
powerful tool for clinical applications. The remarkable performance of our
model underscores its capability to facilitate rapid and reliable brain tumor
diagnoses, potentially streamlining the decision-making process in treatment
planning. These findings strongly support the further investigation and
application of quantum computing and quantum machine learning methodologies in
medical imaging, suggesting a future where quantum-enhanced diagnostics could
significantly elevate the standard of patient care and treatment outcomes.
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