Deep learning and quantum computing are used in the Hybrid Quantum CNN-ResNet152 Model for Enhanced Brain Tumor Diagnosis to make MRI-based brain tumor categorization more accurate. There is room for improvement in the performance of the commonly used traditional CNN and ResNet architectures (ResNet 50, ResNet 101, ResNet 152) in medical imaging. A comparison of various models in this research shows that CNN-ResNet152 outperforms them all with a score of 96.19% F1-score, 97.75% recall, 94.67% precision, and 89.8% accuracy. Data imbalance, computational complexity, and model generalization are still issues, however. One way to tackle this is by incorporating quantum computing methods like QCNN, QVC, and QFE into feature extraction and optimization processes. Hybrid quantum-classical models seem to be a game-changer in the field of medical artificial intelligence and quantum computing, since they considerably enhance brain tumor detection, leading to quicker diagnoses with less ambiguity and better patient outcomes.
Deep learning and quantum computing are used in the Hybrid Quantum CNN-ResNet152 Model for Enhanced Brain Tumor Diagnosis to make MRI-based brain tumor categorization more accurate. There is room for improvement in the performance of the commonly used traditional CNN and ResNet architectures (ResNet 50, ResNet 101, ResNet 152) in medical imaging. A comparison of various models in this research shows that CNN-ResNet152 outperforms them all with a score of 96.19% F1-score, 97.75% recall, 94.67% precision, and 89.8% accuracy. Data imbalance, computational complexity, and model generalization are still issues, however. One way to tackle this is by incorporating quantum computing methods like QCNN, QVC, and QFE into feature extraction and optimization processes. Hybrid quantum-classical models seem to be a game-changer in the field of medical artificial intelligence and quantum computing, since they considerably enhance brain tumor detection, leading to quicker diagnoses with less ambiguity and better patient outcomes.
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.
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 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 tumours are very serious concerns in the health field; they should be diagnosed properly and at the right time to ensure treatment efficacy. While deep learning architectures like ResNet have shown remarkable success in medical imaging, they have their own set of disadvantages, like computational complexity, scalability, and difficulty in processing very high-dimensional medical data. To overcome these hurdles, a hybrid approach has been proposed, combining ResNet with quantum transfer learning, leveraging the embedding to achieve good performance with higher efficiency in the use of quantum computing technology. In this framework, ResNet extracts valuable features from MRI scans, while quantum circuits optimise the classification process, thereby improving performance and generalisation. The quantum component of the proposed model is implemented using the PennyLane framework, enabling seamless integration of quantum circuits with classical deep learning architectures through a hybrid quantum-classical learning paradigm. Extensive experiments show that our hybrid quantum/classical model outperforms all conventional deep learning approaches, achieving higher diagnostic accuracy while minimising computational overhead. Compared to a traditional CNN (a series of neurons processing unstructured data for machine-understandable form) model, which achieves only 82% accuracy, our hybrid model significantly outperforms it with 95% accuracy, representing a 14% increase in classification performance. Results indicate that quantum computing can transform radiology and medical imaging, providing the tools to advance human health and create a future of fast, precise, and scalable diagnostic equipment. This can be considered a huge step forward toward the use of quantum computing in the medical field; it opens another avenue for advanced medical diagnostics and better patient outcomes.
The evolution of quantum computing has paved way for several evolutions, protruding into medical diagnosis too. This paper focuses on Quantum Convolutional Neural Network is compared with existing methods like Support Vector Machine, Convolutional Neural Network and ResNet18 used for brain tumor classification. A detailed study of various types of brain tumor is provided along with possible diagnosis and treatment. The frameworks corresponding to SVM, CNN, ResNet18 and QCNN are provided. The metrics used to evaluate the methods are provided like accuracy, precision, recall, F1 score, confusion matrix and quantum related metrics like Quantum Fidelity, Quantum Cross Entropy Loss, etc. The QCNN is observed to improved accuracy by 10.12%, precision by 10.96%, recall by 3.8%, specificity by 5.4%, F1 score by 7.1%, MSE by 65.5% good results in all parameters varying from 0.1% to 10.96% when compared with SVM, CNN and ResNet18, for multiclass brain tumor classification of Glioma, Meningioma, Pituitary and No Tumor. The quantum fidelity is about 1.0 and the quantum cross entropy is about 0.054. Hence QCNN proves to be a better choice for brain tumor classification.
Medical image classification is considered as very important field of diagnosis and treatment of neurological disorders, which include stroke, tumors, and hemorrhages, it can used to facilitate timely medical intervention. A hybrid quantum-classical convolutional neural network (QCNN) is presented by combining quantum information processing with classical deep learning techniques for improved feature extraction and classification accuracy. The model combines convolutional neural network (CNN), which handles initial feature extraction with a PennyLane and TensorFlow based layer that employs quantum entanglement and superposition principles to enhance classification performance. The model is trained and tested over a computed tomography (CT) scan image dataset which has four classes (normal, stroke, tumor, and hemorrhage). Various learning rates are tested and employed a hybrid backpropagation method to improve efficiency. Confusion matrices, region of conversion receiver operating characteristic (ROC) curves, and plots for the training convergence that all pointed to promising classification accuracy were derived with a detailed analysis accordingly. Overall, the results indicate that combining quantum computing with deep learning architectures could improve classification performance at a lowest computational cost. The results suggest the viability of hybrid quantum-classical models for medical imaging applications and indicate that quantum computing is a promising direction for enhancing diagnostic accuracy in the area of radiology.