Brain Tumor detection knows as tedious job poses huge intricacies. Though MRI(Magnetic Resonance Imaging) has made the life of clinicians easier by providing detailed cross-sections of the brain, thus making it easy to detect abnormalities, it is still very difficult to accurately categorize the different types of Brain Tumors. For addressing this problem, this research work proposes a novel approach to use a Hybrid Quantum Classical Convolutional Neural Network (HQC-CNN) model. The proposed model has been experimented on the BraTS2023 dataset, with a result of 98.7% accuracy, which is an improvement of 5.2% over traditional CNNs (ResNet, VGG). The HQC-CNN model simplifies parameter complexity by 40% and training time by 2.5 times via quantum parallelism. The complete flow is described in the architecture, hybrid training regimes, and issues in deploying QCNNs on near-term quantum hardware in this work. These findings strongly indicates the power of Deep Learning approaches and incorporation of Quantum ideas in medical imaging.
Brain Tumor detection knows as tedious job poses huge intricacies. Though MRI(Magnetic Resonance Imaging) has made the life of clinicians easier by providing detailed cross-sections of the brain, thus making it easy to detect abnormalities, it is still very difficult to accurately categorize the different types of Brain Tumors. For addressing this problem, this research work proposes a novel approach to use a Hybrid Quantum Classical Convolutional Neural Network (HQC-CNN) model. The proposed model has been experimented on the BraTS2023 dataset, with a result of 98.7% accuracy, which is an improvement of 5.2% over traditional CNNs (ResNet, VGG). The HQC-CNN model simplifies parameter complexity by 40% and training time by 2.5 times via quantum parallelism. The complete flow is described in the architecture, hybrid training regimes, and issues in deploying QCNNs on near-term quantum hardware in this work. These findings strongly indicates the power of Deep Learning approaches and incorporation of Quantum ideas in medical imaging.
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.
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.
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) for brain tumor classification using MRI images, leveraging quantum computing for enhanced computational efficiency. A dataset of 3,264 MRI images, including glioma, meningioma, pituitary tumors, and non-tumor cases, was utilized. The data was split into 80% training and 20% testing, with an oversampling technique applied to address class imbalance. The QCNN model consists of quantum convolution layers, flatten layers, and dense layers, with a filter size of 2, depth of 4, and 4 qubits, trained over 10 epochs. Two models were developed: a binary classification model distinguishing tumor presence and a multiclass classification model categorizing tumor types. The binary model achieved 88% accuracy, improving to 89% after data balancing, while the multiclass model achieved 52% accuracy, increasing to 62% after oversampling. Despite strong binary classification performance, the multiclass model faced challenges due to dataset complexity and quantum circuit limitations. These findings suggest that QCNNs hold promise for medical imaging applications, particularly in binary classification. However, further refinements, including optimized quantum circuit architectures and hybrid classical-quantum approaches, are necessary to enhance multiclass classification accuracy and improve QCNN applicability in clinical settings.
Various neurological problems such as brain tumors, strokes, and Alzheimer's disease can all be detected and treated with the help of medical imaging. The level of image analysis determines how accurate and reliable a diagnosis made using Magnetic Resonance Imaging (MRI) will be. As a result, the intricate design of brain structures as well as the presence of noise and aberrations in the recorded pictures might make it difficult at times. This study proposes a Quantum Convolutional Neural Networks (QCNN)-based classification model for brain MRI images. Preprocessing and QCNN-based categorization comprise the two key components of the proposed model. A publicly available real brain MRI dataset is used to test the proposed model. The proposed model is extremely promising, according to the experimental findings. The results also demonstrated that the proposed QCNN outperforms its traditional convolutional neural network counterpart. The proposed model looks very promising, according to the experimental findings. The results additionally demonstrated that the proposed QCNN outperforms its traditional convolutional neural network counterpart. It achieved a 96.1% total accuracy.
This research work discusses three methods for detecting brain tumors using magnetic resonance imaging (MRI) - a pre-trained convolutional neural network (CNN) baseline, a pure quantum machine learning (QML) classifier that utilizes amplitude embedding and variational quantum circuits, and a hybrid of QML with CNN backbone model with a parameterised quantum circuit (PQC). All models were trained and tested on the four-class brain tumor MRI dataset (glioma, meningioma, pituitary, and no tumor) using the same preprocessing and evaluation protocols. The typical classical convolutional neural network (CNN) baseline achieved a test accuracy of 86.1%, with an F1 score of 0.8512. By comparison, the pure QML model provided a rather poor model, achieving limited accuracy of 44.7% and F1 of 0.4320, largely due to issues with shallow variational circuits and amplitude encoding on higher-dimensional data. The hybrid CNN-QML model provided the best results, achieving a test accuracy of 94.9% and an F1 of 0.9469, surpassing the typical classical baseline. These results indicate while standalone QML models are currently limited by the expressivity of quantum circuits and the scalability of their dimensionality, hybrid quantum-classical models can increase medical image classification performance, even when starting from a strong classical baseline.
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.