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.
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.
The problem of classifying brain tumours using magnetic resonance imaging (MRI) is a fundamental one in the field of computeraided diagnosis, since although many commonly employed convolutional neural network (CNN) architectures provide high accuracy they contain a large number of parameters, which has led to a search for more compact network designs. In this paper a hybrid quantum–classical approach is proposed that combines a lightweight CNN feature extractor with a trainable variational quantum circuit (VQC) in order to carry out four-class brain tumour classification. The MRI images are resized to 128 by 128 pixels and then processed by a small three-block CNN that reduces each image to an 8-dimensional feature vector. This vector is put into an 8-qubit quantum register by means of angle embedding and then acted on by a variational circuit composed of strongly entangling layers, by measuring each qubit in the Pauli-Z basis. The quantum features thus obtained are then fed into classical dense layers that categorise the images as glioma, meningioma, pituitary tumour or no tumour. The framework was tested on the Brain Tumor MRI dataset (7,200 images) and compared with a classical CNN baseline that was trained under the same conditions. The hybrid model reached a test accuracy of 76.88%, as compared to 72.31% for the classical baseline an improvement of 4.57 percentage points together with corresponding increases in macro-averaged precision, recall and F1-score. Notably, the hybrid model needed only 72 additional trainable parameters when compared to the classical baseline. The fact that the quantum layer was assessed using a noiseless simulator rather than on actual quantum hardware means that these results should be seen as indicating the feasibility of using compact quantum feature transformations for medical image classification rather than as showing a quantum advantage.
Brain tumor detection is a vital and challenging task in medical diagnosis, requiring highly precise and efficient methods to improve patient treatment outcomes. Traditional approaches rely on classical machine learning and deep learning techniques, which, while effective to a degree, often struggle with capturing the complex and high-dimensional patterns present in medical imaging data. A significant limitation of these conventional methods is their inability to process and analyze intricate features efficiently, which can impact diagnostic accuracy. To address these challenges, this work proposes a novel Quantum Convolutional Neural Network (QCNN) framework that combines quantum computing principles, via quantum circuits, with classical machine learning architectures implemented through tools like PyTorch and PennyLane. This hybrid approach enhances feature extraction and classification capabilities, leading to superior performance. Experimental results demonstrate that the QCNN model achieves an improved accuracy of 90 percent on the test dataset, surpassing traditional methods and showing promise for advancing brain tumor detection technology. This improvement is supported by consistent decreases in training and validation loss during model training, highlighting the robustness and efficacy of the quantum-enhanced model.
Accurate classification of glioma grades from magnetic resonance imaging (MRI) is essential for clinical decision-making in neuro-oncology. Although deep learning performance has been impressive with classical models, they struggle with high-dimensional medical imaging data and generalise poorly beyond their training data, especially in time- and resource-constrained settings. In light of the aforementioned challenges, we propose QuantumMedDx, a hybrid quantum–classical learning framework for classifying gliomas using MRI. The framework combines quantum feature encoding and variational quantum circuits with classical neural inference to improve diagnostic performance. The base model, QImageNet, uses amplitude-based quantum encoding for writing, entanglement-enabled parameterised quantum circuits (EPQCs) as feature extractors, and classical dense layers for classifying HGG and LGG from multimodal MRI slices. We demonstrate the effectiveness of the proposed approach on the BraTS 2021 benchmark dataset using a patient-aware 5-fold cross-validation protocol. Experimental results show that QuantumMedDx achieves accuracies of 94.12%, 93.30%, and 96.42%; F1-scores of 93.30% and 96.42%; and AUCs of 96.42% and 96.42%, respectively, outperforming conventional CNN, DNN, and SVM baselines. Ablation studies provide additional evidence of the performance improvements enabled by quantum Fourier transform and entanglement layers. Such results suggest that quantum–classical learning can efficiently improve feature extraction and discrimination in medical imaging, thus providing a modular and scalable route towards quantum–inspired clinical decision–support systems of the future.
Accurate classification of brain tumors from MRI scans is critical for effective treatment planning. This study presents a Hybrid Quantum Convolutional Neural Network (HQCNN) that integrates quantum feature-encoding circuits with depth-wise separable convolutional layers to analyze images from a publicly available brain tumor dataset. Evaluated on this dataset, the HQCNN achieved 99.16% training accuracy and 91.47% validation accuracy, demonstrating robust performance across varied imaging conditions. The quantum layers capture complex, non-linear relationships, while separable convolutions ensure computational efficiency. By reducing both parameter count and circuit depth, the architecture is compatible with near-term quantum hardware and resource-constrained clinical environments. These results establish a foundation for integrating quantum-enhanced models into medical-imaging workflows with minimal changes to existing software platforms. Future work will extend evaluation to multi-center cohorts, assess real-time inference on quantum simulators and hardware, and explore integration with surgical-planning systems.
The efficiency of quantum computing has recently been extended to machine learning, which has made a significant impact on quantum machine learning. The hybrid structure of quantum and classical ones has developed into the most successful application mode currently due to noisy intermediate scale quantum limitations. In this paper, an improved hybrid quantum-classic convolutional neural network (HQC-CNN) with fast training speed, lightweight, and high performance is proposed. Its convolution layer realizes feature mapping through parameterized quantum circuit, while other layers keep classic operation and finally complete the task of four classifications of brain tumors. The experiment in this paper is based on kaggle brain tumor magnetic resonance imaging public dataset. The final experimental results show that HQC-CNN can effectively classify meningioma, glioma, pituitary, and no tumor with a classification accuracy of 97.8%. When compared to numerous well-known landmark models, HQC-CNN has obvious advantages.
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.