Renal Cell Carcinoma (RCC) is a leading tumour of the kidney and among most common cancer in adults. 30-40% is often diagnosed when already (in) advanced stage, since there is no specific symptoms, but earliest for these patients infancy detection of RCC. This case emphasizes the importance of precise diagnosis from cellular to tubular-epithelial-lining kidney level that results in successful management and good prognosis. Cryofocus and quantum therapy on the background of modern achievements of artificial intelligence and quantum computers in medical attention Recent achievements of Artificial Intelligence (AI) and quantum computer technologies, and medical visualization are solving some problems experienced by the classic methods of treatment long ago. In this paper, we propose a novel quantum networks model (QNM) by AI which joins the quantum enhanced neural computation with high optical resolution medical imaging data for diagnosis of RCC. Here we have shown a quantum version of an innovation for image recognition in which the pixels are not the fundamental unit of the model, but qubits are. G can now learn to represent finer textures, e.g., complex visual patterns that description which is not directly described by CNNs. Pre-processed images from classical computing (histopathological slides, contrast enhanced CT scans) were input to a quantum-classically hybrid system. This architecture features encoding with quantum circuits and variational quantum layers, which learn complex relationships between RCC types of features and epithelial tissue morphology. Our experiments also demonstrate for the first time, the superiority of our hybrid quantum neural model over the classical deep learning methods in various computational sub-tasks of renal tissue anomaly diagnosis — segmentation, classification and grading. Comprehensive validation on heavily curated large-scale datasets consistently shows significantly better accuracy, sensitivity and specificity, which can be in orders of magnitudes, challenging classical models especially in regions where they diverge. Feedback from expert clinicians (oncologists and pathologists) indicates very good correlation of quantum-based features to pathological RCC biomarkers. Their work is a step toward something that kills so many people each year, and something vastly more accurate and efficient to diagnose. Quantum AI, medical imaging and combination are part of an era-defining decision-supporting system. It might mean earlier detection, less false positives and better outcomes for RCC patients. This model is sector independent and can be generalised to other cancer types and imaging modalities as well.
Renal Cell Carcinoma (RCC) is a leading tumour of the kidney and among most common cancer in adults. 30-40% is often diagnosed when already (in) advanced stage, since there is no specific symptoms, but earliest for these patients infancy detection of RCC. This case emphasizes the importance of precise diagnosis from cellular to tubular-epithelial-lining kidney level that results in successful management and good prognosis. Cryofocus and quantum therapy on the background of modern achievements of artificial intelligence and quantum computers in medical attention Recent achievements of Artificial Intelligence (AI) and quantum computer technologies, and medical visualization are solving some problems experienced by the classic methods of treatment long ago. In this paper, we propose a novel quantum networks model (QNM) by AI which joins the quantum enhanced neural computation with high optical resolution medical imaging data for diagnosis of RCC. Here we have shown a quantum version of an innovation for image recognition in which the pixels are not the fundamental unit of the model, but qubits are. G can now learn to represent finer textures, e.g., complex visual patterns that description which is not directly described by CNNs. Pre-processed images from classical computing (histopathological slides, contrast enhanced CT scans) were input to a quantum-classically hybrid system. This architecture features encoding with quantum circuits and variational quantum layers, which learn complex relationships between RCC types of features and epithelial tissue morphology. Our experiments also demonstrate for the first time, the superiority of our hybrid quantum neural model over the classical deep learning methods in various computational sub-tasks of renal tissue anomaly diagnosis — segmentation, classification and grading. Comprehensive validation on heavily curated large-scale datasets consistently shows significantly better accuracy, sensitivity and specificity, which can be in orders of magnitudes, challenging classical models especially in regions where they diverge. Feedback from expert clinicians (oncologists and pathologists) indicates very good correlation of quantum-based features to pathological RCC biomarkers. Their work is a step toward something that kills so many people each year, and something vastly more accurate and efficient to diagnose. Quantum AI, medical imaging and combination are part of an era-defining decision-supporting system. It might mean earlier detection, less false positives and better outcomes for RCC patients. This model is sector independent and can be generalised to other cancer types and imaging modalities as well.
Renal cell carcinoma (RCC) is a lethal urological cancer, and high mortality rates associated with the disease are frequently due to late diagnosis and the functional diversity of a tumor. Current diagnostic paradigms, mostly depending on radiological visualization and histopathological evaluation, are plagued with problems in the form of inter-observer variation and lag time in detection, and are also inadequate in recapitulating the heterogeneous nature of the tumor microenvironment. To address these challenges, in this work we propose an intelligent diagnostic framework combining quantumenhanced expandable neural networks (Q-ENNs) and medical imaging for precision oncology. In contrast to traditional DL models, Q-ENNs leverage quantum parallelism and entanglement-induced strategies to traverse high-dimensional imaging spaces by outperforming feature extraction and diagnostic performance. The scalable design can flexibly scale network layers and qubits according to clinical requirements, allowing multi-stage RCC diagnosis. Q-ENNs trained on multimodal imaging data (including CT, MRI, and histopathological slides) outperform state-of-theart convolutional neural networks (CNNs) in the tasks of tumor detection, staging, and subtype classification. In addition, the quantum-assisted optimization approach accelerates the convergence and alleviates local minima, which are not only benefit for better discrimination performance but also robust diagnostic results among different populations of patients. We validate our results by clinical evaluation, where a better trade-off between sensitivity and specificity with higher interpretability are obtained with respect to state-of-the-art through explainable quantum feature mapping, as claimed in precision oncology terms. The system we present is intended to be deployed in a realistic clinical setting, supporting computational performance (scalability), integration in the existing radiology workflow, and generic imaging modalities. By interfacing principles of quantum computing with clinical applications, this study paves the road for the future of oncology diagnostics. These discoveries underpin that quantum-driven diagnostic systems could change the way RCC is assessed, providing for earlier and more reliable detection, help in the personalization of treatment and thus enhance patient outcomes in precision oncology.
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.
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.
Cancer continues to be one of the most pressing health challenges worldwide, where early detection plays a crucial role in improving patient outcomes and survival rates.This paper presents a Quantum AI Cancer Detection System designed to assist medical professionals in identifying cancerous tissue from histopathology images efficiently.The proposed system uses deep learning techniques, specifically a Convolutional Neural Network (CNN), to classify tissue samples as cancerous or non-cancerous.It is integrated into a user-friendly web application that enables healthcare professionals to upload images and receive instant predictions along with confidence scores, risk levels, health guidance, and downloadable reports.The model demonstrates reliable performance on the available dataset and helps to reduce the time and effort required for manual diagnosis.However, further improvements can be achieved by training on larger and more diverse datasets to enhance generalization.Future enhancements include tumor localization using Grad-CAM, integration of genomic data, and exploration of quantum machine learning techniques for more accurate and comprehensive cancer diagnostics.
Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facing limitations in complex thermal pattern classification tasks. This paper presents a novel Hybrid Quantum Neural Network (HQNN) architecture that integrates quantum computing principles with classical convolutional neural networks for enhanced breast cancer classification. Our approach employs parameterized quantum circuits with multi-head attention mechanisms for quantum-aware feature encoding, coupled with classical convolutional layers for comprehensive pattern recognition. The quantum component utilizes a 4qubit variational circuit with strongly entangling layers, while the classical component incorporates advanced attention mechanisms for feature fusion. Experimental validation on breast cancer thermographic data demonstrates substantial performance improvements over state-of-the-art classical architectures, with the quantum-enhanced approach exhibiting superior convergence dynamics and enhanced feature representation capabilities. Our findings provide evidence for quantum advantage in medical image classification through classical simulation, establishing a framework for quantum-classical hybrid systems in healthcare applications. The methodology addresses key challenges in quantum machine learning deployment while maintaining computational feasibility on near-term quantum devices.
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.