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.
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.
Abstract This study demonstrates that a hybrid CNN–QNN architecture outperforms classical CNNs for multi-class retinal OCT classification by leveraging variational quantum circuits to enhance feature separability. Early detection of retinal diseases through optical coherence tomography (OCT) imaging is critical for timely intervention and vision preservation. While classical convolutional neural networks (CNNs) excel at pattern recognition in retinal OCT B-scans, their ability to capture complex, non-linear feature relationships in subtle disease manifestations remains limited. Hybrid quantum-classical architectures offer a promising approach by leveraging variational quantum circuits to enhance feature separability in challenging classification regimes. This study presents a hybrid CNN–QNN architecture for multi-class classification of retinal OCT images dataset (CNV, DME, DRUSEN, NORMAL). A classical CNN backbone extracts hierarchical spatial features from B-scans, which are then processed through parallel classical and quantum classification heads. The quantum head employs a 5-qubit variational quantum circuit with angle embedding and strongly entangling layers to generate quantum-enhanced feature representations. Outputs from both heads are concatenated and passed through a final softmax layer for four-class prediction. The findings establish hybrid quantum-classical neural networks as a viable enhancement to conventional deep learning for retinal OCT analysis, offering improved discriminative performance on established medical imaging benchmarks through quantum feature transformations.
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.
Abstract Leukemia is a life-threatening blood cancer that requires early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods based on manual examination of blood smear images are time-consuming and prone to human error. With the advancement of artificial intelligence, automated detection systems using machine learning and deep learning techniques have shown promising results. However, classical models face limitations in handling complex feature spaces efficiently. To address this, this project proposes a hybrid approach titled “Leukemia Detection using Quantum Machine Learning”, which integrates classical image processing with quantum computing techniques. In this work, microscopic blood smear images from a publicly available Acute Lymphoblastic Leukemia dataset are utilized. The images undergo preprocessing steps including resizing, noise removal using Gaussian filtering, and normalization to enhance data quality. A Convolutional Neural Network (CNN) is employed for classical feature extraction, transforming image data into meaningful feature vectors. These features are further reduced using Principal Component Analysis (PCA) to make them suitable for quantum processing. The reduced feature set is encoded into quantum states using both angle encoding and amplitude encoding techniques. Quantum properties such as superposition and entanglement are leveraged through parameterized quantum circuits. Two quantum machine learning models, namely Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN), are implemented to perform multi-class classification of leukemia stages, including Benign, Early, Pre, and Pro. The performance of both models is evaluated and compared using metrics such as accuracy, confusion matrix, and classification report. The proposed hybrid classical-quantum framework demonstrates the potential of quantum machine learning in medical image analysis. By combining deep learning with quantum algorithms, the system aims to improve diagnostic accuracy and computational efficiency. This approach not only enhances leukemia detection but also highlights the applicability of quantum computing in real-world healthcare problems, paving the way for future advancements in intelligent medical diagnosis systems.
Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image 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.