Recent breakthroughs in medical computational modelling, particularly in ophthalmology, have produced remarkable achievements in disease identification and classification with the aid of deep learning and hybrid quantum–classical methods. Though the current ensemble CNNs have more than 99% accuracy in diagnosing glaucoma, diabetic retinopathy, and other retinal diseases, scalability and interpretability remain problematic. This paper presents a novel hybrid quantum–classical approach that combines quantum feature encoding with ensemble CNNs optimised to improve diagnostic accuracy and lower computational burden. The new model exhibits higher feature extraction efficiency and noise robustness than traditional architectures. Through the use of quantum state representations, the system enables better decision-making, prediction, and personalised therapy suggestions. Integrated automated analysis of behaviour in this system also supports real-time identification and adaptive treatment planning.In general, the main innovation is the integration of quantum encoding and ensembles of deep learning for the classification of ophthalmic disease, providing a scalable avenue toward intelligent, real-time healthcare analysis.
Recent breakthroughs in medical computational modelling, particularly in ophthalmology, have produced remarkable achievements in disease identification and classification with the aid of deep learning and hybrid quantum–classical methods. Though the current ensemble CNNs have more than 99% accuracy in diagnosing glaucoma, diabetic retinopathy, and other retinal diseases, scalability and interpretability remain problematic. This paper presents a novel hybrid quantum–classical approach that combines quantum feature encoding with ensemble CNNs optimised to improve diagnostic accuracy and lower computational burden. The new model exhibits higher feature extraction efficiency and noise robustness than traditional architectures. Through the use of quantum state representations, the system enables better decision-making, prediction, and personalised therapy suggestions. Integrated automated analysis of behaviour in this system also supports real-time identification and adaptive treatment planning.In general, the main innovation is the integration of quantum encoding and ensembles of deep learning for the classification of ophthalmic disease, providing a scalable avenue toward intelligent, real-time healthcare analysis.
Quantum-Enhanced Machine Learning (QEML) is a new computational paradigm that introduces the concept of quantum mechanics with the latest artificial intelligence algorithms to enhance the performance of the diagnostic process in the field of medical imaging. The early identification of diseases is crucial in mortality reduction and improving patient outcome, especially in capacities dealing with such diseases as cancer, neurological diseases, heart diseases, and infections of the lungs. Although conventional deep learning architectures, in particular, Convolutional Neural Networks (CNNs) have been highly successful in processing complex medical images such as MRI scans, CT images, X-rays, and histopathological data, they can be computationally expensive, need large data sets, and suffer in optimization of high dimensional feature spaces. The limitations that QEML tries to overcome are based on the quantum mechanical principles of superposition, entanglement and interference to facilitate better data representation and processing. The suggested structure is based on the classical deep learning architectures and variational quantum circuits together, to create a hybrid quantum-classical system, which improves features extraction and classification levels. Under this method, the classical image characteristics are coded into quantum states and processed in high-dimensional Hilbert spaces, which can enhance the separability of subtle disease patterns, which would have otherwise been challenging to obtain with conventional techniques. The objective of this hybrid system is to enhance the critical diagnostic measures like sensitivity and specificity without affecting the interpretability of such measures to be used in the clinical context. The proposed framework is also assessed in terms of its feasibility, its computational performance and scalability to the capabilities of present-day Noisy Intermediate-Scale Quantum (NISQ) devices. The model effectiveness is evaluated using objective evaluation metrics, which, in a way, guarantee the feasibility of the model application within a real-life healthcare context. On balance, quantum computing and medical image analysis integration is an exciting field of work of the future intelligent healthcare systems and has a potential of providing more efficient, accurate, and scalable diagnostic solutions.
Diabetic Retinopathy (DR) is a severe complication of diabetes, characterized by damage to the blood vessels in the retina, leading to an increased risk of vision loss or blindness. Patients with diabetes who develop DR face a significant risk of visual impairment, underscoring the need for advanced and reliable diagnostic tools. This research addresses this need by introducing a hybrid Quantum Convolutional Neural Network (QCNN) model, designed specifically for the automated detection of DR. By leveraging quantum principles and classical deep learning, this model provides a powerful approach to screening for diabetic retinopathy. In a Convolutional Neural Network (CNN), convolutional layers are primarily responsible for feature extraction from images, capturing intricate details essential for accurate classification. However, the proposed hybrid QCNN introduces quantum layers in addition to traditional convolutional layers, combining the strengths of both quantum computing and classical neural network structures. Quantum computing principles, such as superposition and entanglement, allow the QCNN to encode and process information in unique ways, enhancing the efficiency of feature extraction and the overall performance of the model. Superposition enables the QCNN to represent and manipulate multiple states simultaneously, while entanglement allows for a more complex, interconnected representation of image features. These quantum properties enable the QCNN to explore a broader feature space, offering richer representations of the retinal images than those achievable through classical methods alone. Evaluation of the model’s performance reveals that the QCNN surpasses a traditional CNN in both accuracy and diagnostic performance. This improvement suggests that hybrid QCNN models hold significant promise for enhancing diabetic retinopathy screening, paving the way for earlier diagnosis and better patient outcomes. Future research will focus on refining the hybrid QCNN model to improve its robustness and adaptability, aiming to ensure its effectiveness in real-world healthcare environments.
Traditional convolutional neural networks (CNNs) have shown potential for recognizing retinopathy caused by diabetes (DR). However, developing quantum computing has the possibility for improved feature representation. We propose a hybrid approach that combines classical CNNs with quantum circuits to capitalize on both classical and quantum information for DR classification. Using the Keras and Qiskit frameworks, our model encodes picture features into quantum states, allowing for richer representations. Through experiments on a collection of retinal pictures, our model displays competitive performance, with excellent reliability and precision in categorizing DR severity levels. This combination of classical and quantum paradigms offers a fresh approach to enhancing DR diagnosis and therapy.
Glaucoma is a progressive optic neuropathy that leads to irreversible vision loss if not detected early. Automated analysis of retinal images plays an important role in detecting glaucoma-related structural abnormalities; however, conventional image processing and classical machine learning methods often struggle to model complex nonlinear relationships among clinical biomarkers. This study presents a quantum enhanced diagnostic framework for glaucoma detection that encodes retinal structural features into quantum states and analyzes them using quantum machine learning techniques. Optical Coherence Tomography (OCT) provides high-resolution cross-sectional visualization of retinal layers and facilitates quantitative assessment of key biomarkers, including Retinal Nerve Fiber Layer (RNFL) thickness, Ganglion Cell-Inner Plexiform Layer (GCIPL) thickness, and Bruch's Membrane Opening-Minimum Rim Width (BMO-MRW). After normalization, the features are transformed into quantum states via angle encoding and subsequently processed within an entangled quantum circuit. Classification is performed using a Quantum Support Vector Machine (QSVM) with a fidelity-based quantum kernel. Experimental evaluation shows that the proposed model achieves a classification accuracy of 73% on the dataset that shows promising results over classical baseline SVM model. Although classical deep learning models often report higher accuracies, this work demonstrates the feasibility of representing clinically interpretable OCT biomarkers within quantum feature spaces. The study highlights the potential of quantum kernel methods for structured ophthalmic data and provides an initial step toward scalable quantum-assisted glaucoma diagnostic systems as quantum computing technology continues to evolve.
Parkinson's disease (PD) is a progressive neuro-degenerative condition with an urgent need for timely and pinpoint diagnosis for adequate treatment. Despite potential for prediction with traditional machine learning techniques, PD data remains difficult to solve for traditional machine learning techniques due to the high computational complexity and the nature of data. We introduce in this work a Hybrid Quantum-Classical AI Framework that combines advantages of QC and provide benefits in predictive accuracy as well as computational efficiency for real time Parkinson's disease detection. We use classical deep learning models in conjunction with Variational Quantum Circuits (VQC) to perform efficient feature extraction and classification of biomedical signals: speech and its recordings, gait analysis, and EEG data. We evaluate the performance of our proposed framework against deep learning models that are standard and based on the accuracy, precision, recall and processing time. Our experimental results suggest that our hybrid quantum classical model outperforms classical methods for both accuracy and efficiency, demonstrating the potential of quantum machine learning (QML) in healthcare. This work gives a taste of the quantum revolution realized in medical diagnostics and paves the way for what could in the future be quantum enhanced disease prediction.
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.