Brain tumor classification remains a challenging task due to the complex heterogeneity of glioma characteristics and the limitations of conventional machine learning approaches in effectively integrating multimodal biomedical data. Recent advances in quantum machine learning have demonstrated promising capabilities for handling high-dimensional data; however, existing models often suffer from limited feature representation, insufficient multimodal integration, and optimization challenges. This paper proposes an Enhanced Hybrid Quantum–Classical Architecture (EHQCA) for glioma classification by combining MRI imaging features and TCGA molecular biomarkers within a unified learning framework. The proposed methodology employs ResNet-50 for imaging feature extraction, ensemble feature selection for molecular biomarkers, attention-based multimodal fusion, adaptive amplitude encoding, and a Multi-Layer Variational Quantum Classifier (ML-VQC). A hybrid Adam-SPSA optimization strategy is introduced to improve convergence speed and training stability. Experimental analysis demonstrates that the proposed EHQCA framework achieves superior classification performance, attaining 91.2% accuracy while reducing training epochs compared with existing quantum and classical models. The results highlight the effectiveness of multimodal fusion and quantum-enhanced learning for improving brain tumor diagnosis and supporting precision medicine applications
Brain tumor classification remains a challenging task due to the complex heterogeneity of glioma characteristics and the limitations of conventional machine learning approaches in effectively integrating multimodal biomedical data. Recent advances in quantum machine learning have demonstrated promising capabilities for handling high-dimensional data; however, existing models often suffer from limited feature representation, insufficient multimodal integration, and optimization challenges. This paper proposes an Enhanced Hybrid Quantum–Classical Architecture (EHQCA) for glioma classification by combining MRI imaging features and TCGA molecular biomarkers within a unified learning framework. The proposed methodology employs ResNet-50 for imaging feature extraction, ensemble feature selection for molecular biomarkers, attention-based multimodal fusion, adaptive amplitude encoding, and a Multi-Layer Variational Quantum Classifier (ML-VQC). A hybrid Adam-SPSA optimization strategy is introduced to improve convergence speed and training stability. Experimental analysis demonstrates that the proposed EHQCA framework achieves superior classification performance, attaining 91.2% accuracy while reducing training epochs compared with existing quantum and classical models. The results highlight the effectiveness of multimodal fusion and quantum-enhanced learning for improving brain tumor diagnosis and supporting precision medicine applications
This research presents a new hybrid model that combines the capabilities of a pre-trained ResNet-50 with a Quantum Neural Network (QNN) to enhance the classification of MRI brain images for tumor recognition. Combining conventional deep learning and quantum computing, this technique competes with traditional models regarding accuracy, speed, and computational effectiveness. The ResNet-50 architecture is the foundation for feature extraction, using its demonstrated ability to handle challenging MRI image data. ResNet-50's linear layer weights are converted into angular parameters to take advantage of the potential of quantum computing, allowing the quantum layer to function within a six-angle depth framework. This unique integration allows the model to efficiently execute binary classification and multi-class tumor detection. The hybrid model outperforms conventional models, providing quicker processing speeds and higher classification accuracy, especially when dealing with high-dimensional and noisy data. The model optimizes 512-dimensional data using four qubits, allowing for improved feature representation and better insights into complicated medical imaging patterns. The quantum layer's resistance to noise enhances the model's accuracy, establishing a new standard for medical diagnostic tools. This novel integration of classical and quantum approaches offers a significant advance in MRI-based tumor identification, providing higher performance and opening the path for next-generation medical imaging systems.
Abstract Gliomas, which are the most common malignant primary brain tumors, present significant challenges in terms of varying survival rates, treatment modalities, and prognostic processes between patients with low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification and grading of LGGs and HGGs are crucial for appropriate treatment planning and assessment of overall prognosis. The classification and grading of gliomas have undergone evolution over time, and the inclusion of molecular markers in the classification of glioma tumors by the WHO Central Nervous System (CNS) Tumors Classification in 2016 and the incorporation of advanced molecular diagnostics in 2021 have improved glioma tumor characterization. However, the high cost and limited accessibility of molecular genetic tests, coupled with the time-consuming nature of obtaining results, can lead to delays in critical treatment decisions. To address these challenges, various classical artificial intelligence methods, such as machine learning and deep learning, have been applied to problems in this field, yielding a certain level of success. Nevertheless, the continuous expansion of medical data dimensions, the inherent noise level in the data, and the limitations of the classical vector space pose significant challenges that classical artificial intelligence methods struggle to overcome. Recent studies have demonstrated that the use of quantum computing and quantum artificial intelligence technologies in healthcare not only addresses these problems but also accelerates complex data analyses and processes large datasets more efficiently. This paper presents a novel hybrid quantum (or classical-quantum) computing model aimed at differentiating between LGGs and HGGs using data from The Cancer Genome Atlas (TCGA). To the best of our knowledge, this study is the first to investigate the classification of LGGs and HGGs using a hybrid classical-quantum framework within the TCGA dataset. In the classical part of the study, an ensemble feature selection method was used to identify the most important molecular markers and clinical features within the TCGA glioma dataset. In the quantum section, six variational quantum classifier (VQC) models with different hyperparameters are proposed. These classifiers are subsequently used to differentiate between LGGs and HGGs using the features obtained from the ensemble model. The computational results show that among the six VQC models, the VQC-1 model, which incorporates Rx and CX gates in the feature map and Ry, Rz and CY gates in the parameterized quantum circuit and utilizes the AQCD optimization method, achieves the highest classification accuracy of 0.74. This study provides a novel perspective on the classification of glioma tumors by combining classical and quantum computing methods.
Accurate and computationally efficient classification of brain tumors from MRI scans is critical for deployment in resource-constrained clinical settings, where conventional deep CNN architectures-despite achieving strong accuracy-often require large parameter counts that limit scalability. This work proposes a hybrid quantum-classical deep learning framework that combines a classical convolutional feature extractor with a variational quantum circuit (VQC) for multi-class tumor classification. Spatial features extracted by the CNN backbone will be dimensionality-reduced and encoded into quantum states using angle encoding; a parameterized quantum circuit will then process these features in a higher-dimensional Hilbert space, leveraging superposition and entanglement to capture non-linear feature correlations. Measurement outputs from the quantum layer will be passed to a classical fully connected layer for classification into glioma, meningioma, pituitary tumor, and no-tumor categories. The proposed framework will be evaluated on a publicly available benchmark MRI dataset and benchmarked against state-of-the-art classical CNN architectures in terms of accuracy, precision, recall, F1-score, and parameter efficiency. This study aims to demonstrate that quantum-enhanced layers can achieve competitive classification performance while substantially reducing trainable parameters, offering a practical direction for efficient, quantum-assisted medical image analysis.
The characterization of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and prognosis. But the analysis of brain tumors based on MRI is still difficult because of the significant intra-tumoral heterogeneity, ambiguous boundaries of lesions, high dimensionality of imaging features, and complicated nonlinear relationships between anatomical structures. While transformer-based deep learning (DL) models have greatly advanced automated tumor analysis, classical feature representations often fail to effectively capture complex feature interactions in pathological images, especially when distinguishing between subtle pathological variations and normal tissue needs to be done efficiently. Recent quantum machine learning (QML) developments indicate that quantum feature encoding can map complex data to exponentially larger Hilbert spaces, leading to a more expressive representation of nonlinear patterns and feature correlations that are challenging to model with conventional architectures. Encouraged by this potential, this study proposes a hybrid quantum-classical framework, named QFormer-Brain, which combines transformer-based segmentation with quantum representation learning for automated brain tumor segmentation and classification. To segment the tumor regions, a Shifted Window UNet Transformers (Swin-UNETR) architecture is first used to capture both local anatomical details and global contextual information through hierarchical self-attention. Then, the deep semantic features of the segmented lesions are mapped to the quantum feature mapping module with angle encoding and variational quantum circuits (VQCs), which can obtain richer nonlinear feature representation by quantum superposition and entanglement mechanisms. The obtained quantum embeddings are then combined with features obtained from the transformer and classified by a Quantum Transformer Classifier. The experiments were performed on the publicly available BraTS 2021 data set using an 8-qubit variational quantum circuit, with 1024 measurement shots and readout-error mitigation, via the IBM Qiskit Aer simulator. The proposed framework achieved a Dice Similarity Coefficient (DSC) of 97.1%, Intersection-over-Union (IoU) of 95.0%, classification accuracy of 98.5%, sensitivity of 98.1%, specificity of 99.0%, and F1-score of 98.3%, outperforming U-Net, nnU-Net, ResNet50, Vision Transformer (ViT), and conventional hybrid Convolutional Neural Network (CNN)-based models. These results show that quantum-inspired representation learning can be efficiently integrated into transformer-based models to boost the discriminative feature extraction from complex MRI data.
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.
Brain tumor detection from MRI images is a critical and challenging task in medical diagnostics, where early identification greatly improves treatment outcomes. This paper presents a Hybrid Quantum– Classical Convolutional Neural Network (HQCNN) that integrates the strengths of ResNet-18 and a Variational Quantum Circuit for efficient and accurate tumor classification. The proposed model first employs ResNet-18 as a classical feature extractor, where 17 convolutional layers and pooling operations capture both low-level and high-level spatial features from MRI images. The final fully connected layer of ResNet-18 is replaced by a quantum processing layer implemented using PennyLane. In this layer, the extracted classical features are encoded onto qubits using RY rotation gates, while CNOT gates introduce entanglement between them, forming a VQC with trainable parameters. After quantum computation, Pauli-Z measurements convert the processed quantum states back into classical values, which are passed to a final fully connected layer for binary classification as Tumor or No Tumor. The hybrid integration of classical CNN feature learning with quantum entanglement– based representation enhances model generalization and interpretability. Experimental analysis demonstrates that the proposed HQCNN achieved 94.65% accuracy, outperforming traditional CNN models and confirming the potential of hybrid quantum–classical architectures for reliable and interpretable medical image diagnosis.