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.
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.
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.
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.
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.
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.
Magnetic Resonance Imaging (MRI) images reveal unique abnormal patterns in brain tumors. These patterns play an important role in diagnosis and therapy planning. This study proposed a new model that combines Convolution Neural Network (CNN) and Parameterized Variational Quantum Circuit (VQC) to better diagnose and categorize brain tumors. The model extracted features from MRI images using pre-trained systems such as VGG16, VGG19, and ResNet-18. Among these models of CNN, the base model was selected as VGG16 for feature extraction which yield better performance. The features were subsequently reduced via an affine transformation and passed through a VQC for the hybrid model. The VQC used quantum superposition and entanglement as tools for categorization. The hybrid model performed better than base model due to the representation of feature in large space called Hilbert space. Using n qubit of quantum, 2n states were represented in the Hilbert space. Using VQC, the complex high dimensional relationship of features was learnt and also the performance of the hybrid model was optimized by integrating VQC to VGG16. The experiment was done integrating the pennylane simulator with pytorch.
Furthermore, the physics and multimodality imaging characteristics of tumors, along with high variability in intensity, morphology and anatomical position of the tumor, make the diagnosis of tumors from magnetic resonance imaging (MRI) still quite difficult. In this study, we introduce a hybrid quantum-classical Quantum Convolutional Neural Network (QCNN) that applies Quantum processing in the localized stage of feature extraction rather than later in the classification stage when most current quantum neural networks (CNNs) are used, for automated tumor detection from multiparametric MRI data. The proposed framework makes use of the BraTS 2021 data set, comprising of T1-weighted, post-contrast T1-weighted (T1Gd), T2-weighted and T2-FLAIR images, along with patient-level partitioning, intensity normalization, tumor relevant slice selection, 4×4/8×8 patch generation, dimensionality reduction, quantum angle encoding, trainable variational quantum transformations and Pauli-Z expectation-value measurements. This produced quantum feature maps which were summed across classical layers, and compared to a standard CNN and transfer learning CNN and CNN with a quantum classifier. The proposed QCNN showed excellent performance with 96% accuracy, 93% sensitivity, 94% specificity, 94% precision, 94% F1-score, AUPRC of 0.94, AUROC of 0.96 and the MCC score is 0.82, which is the best among all the architectures considered. The use of architectural analysis revealed to be an option, since 4-6 qubits, 2-3 quantum layers, angle encoding and linear or ring entanglement prove to be adequate. The results of robustness analysis showed that the performance is degrades progressively as the level of quantum noise is increased but still reached a good AUROC (0.80) in the case of strong quantum noise, and was confirmed by an independent external test with a value of 0.85. The results here show trainable quantum convolution to be one potential approach to localized multimodal representation learning for MRI, and present an indication of the impact of quantum noise, finite shot execution, and dataset shift. The study provides a groundwork for the further expansion of research into the use of quantum-enhanced MRI for analysis, in the form of an experimentally controlled experiments design, in larger independent cohorts of patients and with real quantum infrastructure.