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.
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.
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.
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.
Quantum machines enhance the capabilities of classical counterparts across various domains, notably in addressing real-world challenges.The classification of brain MR images for tumor detection is a crucial diagnostic process in the analysis of brain images.Traditional approaches, such as classical machine learning techniques and conventional deep learning structures like convolutional neural networks, are frequently employed for image classification.However, as the network size increases, training these models becomes increasingly arduous.Quantum algorithms offer advantages by optimizing the performance of classical algorithms through the incorporation of the intrinsic properties of quantum bits.In this paper, we proposed a hybrid classical and quantum convolutional neural network for Alzheimer's disease (AD) classification.The proposed model was further validated on the brain tumor classification task.The fundamental concept involves encoding data into quantum states, facilitating quicker information extraction, and subsequently utilizing this information to discern the data class.The proposed model results underscore the reliability and robustness and demonstrated by optimal performance accuracies across various datasets, the proposed model substantiates its efficacy in detecting and classifying AD disease and brain tumors.
Medical imaging plays an important role in the early diagnosis of various diseases. In machine learning algorithms, texture, color, and shape features are primarily used. This may result in a system with inadequate model generalization capabilities. The development of an end-to-end model that can classify unprocessed medical images is now possible by recent advances in deep learning. The heavy demands of deep learning on memory and computer resources result in further advancement in the processing of complex data. As a result, quantum computing offers potential solutions that take advantage of quantum mechanics concepts like entanglement, superposition, and interference. This chapter introduces a new medical image classification model based on the quantum convolutional neural network (QCNN). Three benchmark datasets are used to evaluate the performance of QCNN; PneumoniaMNIST, VesselMNIST3D, and brain tumor MRI images. The experimental results showed that the proposed model based on the proposed QCNN is very promising.
Quantum Convolutional Neural Networks (QCNNs) have became a promising field for quantum advantage in feature extraction and classification of different datasets like Handwritten datasets, Fashion datasets, particularly useful for complex datasets like medical images. Medical image classification has emerged as a critical component in modern healthcare diagnostics, particularly for tumor detection and cancer diagnosis. Normally, Classical CNN is used for image classification but they may face certain challenges in achieving further improvements in accuracy, computational efficiency, processing time. Also, it becomes difficult to train for high-dimensional medical datasets and extracting complex feature representations. Therefore, In this proposal, Image classification through a Scalable and Resource efficient QCNN is proposed which integrates classical CNN with quantum computing. This review examines the current state of medical image classification for tumor detection, analyzes the transition from classical CNNs to quantum-based architectures, and explores the potential of QCNNs in revolutionizing medical diagnostics. This research includes QCNN architecture which will include selective feature encoding using encoding technique such as Z Feature map and quantum circuits for convolutional layers and pooling layers to handle multi-modal medical data (e.g., MRI and CT scans). We aim to achieve superior accuracy with reduced parameters over classical CNNs. This work advances quantum ML toward practical deployment in resource-limited healthcare settings.
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.