An ablation study on hybrid quantum neural networks for enhancing brain tumour diagnosis
Veerakumar Pandi, Kalaiselvi Thiruvenkadam · The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · 2025
The proposed work developed a robust hybrid quantum neural network model (HQNN) to mitigate the model overfitting problem for MRI brain tumour diagnosis. As quantum computing is the future of computation, the proposed work analyzed the impact of quantum layers on HQNN architectures and quantum advantages in improving model generalisation through an ablation study. We designed a single-layered shallow 10-Qubit parameterised quantum circuit (PQC) to reduce the model’s overfitting. Fifteen HQNN models were designed, incorporating a diverse set of quantum gate arrangements to explore their effects on model performance. Through ablation experiments, the proposed work utilised the substantial impact of single-layered shallow PQC with RX rotation, H-Gate superposition, and CZ entanglement in improving model expressivity and more stable performance on validation data, and enhanced the model's robustness. The proposed PQC compensates for the reduced regularisation effect caused by turning off 10% of neuron activations at each training step in the HQNN model. It improved the validation accuracy of the model by 4.73%, indicating better generalisation than classical FCNN models. The proposed work used brain tumour classification datasets from Kaggle and figshare repositories. We compared our HQNN model with state-of-the-art (SOTA) methods on standard MRI brain tumour datasets. The proposed HQNN model outperformed the SOTA methods with 98.17% test accuracy in binary classification and 96.97% test accuracy in multi-class classification.