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This research aims to enhance brain tumour classification through quantum-augmented deep learning models.

March 30, 2026Open Access

Brain Tumour Classification with Quantum-Augmented Deep Learning Model

Key Points

This research aims to enhance brain tumour classification through quantum-augmented deep learning models.
Utilized quantum layers integrated into deep learning models
Compared hybrid MobileNetV2 against traditional CNN models
Evaluated model performance based on validation accuracy and computational efficiency.
Hybrid MobileNetV2 achieved the highest validation accuracy of 95.79%
Outperformed traditional CNN baselines
Demonstrated faster inference and reduced computational overhead.

Abstract

Brain tumours are life-threatening conditions that demand early and precise diagnosis to improve patient outcomes. While deep learning has significantly advanced automated medical imaging, conventional convolutional neural network (CNN) models often require large annotated datasets and intensive computation, limiting their applicability in clinical settings. In experiments, the quantum-augmented models achieved notable performance gains. The hybrid MobileNetV2 model achieved the highest validation accuracy of 95.79%, outperforming traditional CNN baselines while offering faster inference and reduced computational overhead. These results suggest that integrating quantum layers enhances feature representation and model robustness.

Brain Tumour Classification with Quantum-Augmented Deep Learning Model

Key Points

Abstract

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