Quantum CNNs achieve 99% accuracy in medical diagnostics

Researchers have developed a hybrid classical-quantum framework for medical image classification, integrating transfer learning with quantum convolutional neural networks (QCNNs). This approach was tested on kidney disease, cervical cell, and brain tumor diagnoses, achieving high test accuracies of 99%, 97%, and 99% respectively. The quantum-enhanced models demonstrated superior performance compared to classical CNNs, requiring fewer trainable parameters while maintaining precision, recall, and F1 scores. AI

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IMPACT Demonstrates potential for quantum computing to enhance diagnostic accuracy and efficiency in medical imaging.

RANK_REASON Academic paper presenting a novel hybrid classical-quantum approach for medical image classification with experimental results.

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• arXiv
• QCNN
• CNN
• Shabnam Sodagari

• paper
• other