Peripheral blood cell classification is critical for computer-aided haematological diagnosis. In the present study, a hybrid classical-quantum deep learning framework for multi-class classification using the BloodMNIST dataset. Operating directly on native 28×28 images, our pipeline preserves morphological features while minimizing computational complexity. A lightweight convolutional network extracts features that are normalized, reduced via Principal Component Analysis, and encoded into an 8-qubit variational quantum circuit (HybridQNet). The proposed model achieves 96.26% validation and 95.12% test accuracy, demonstrating an efficient, scalable approach for biomedical image analysis.
Peripheral blood cell classification is critical for computer-aided haematological diagnosis. In the present study, a hybrid classical-quantum deep learning framework for multi-class classification using the BloodMNIST dataset. Operating directly on native 28×28 images, our pipeline preserves morphological features while minimizing computational complexity. A lightweight convolutional network extracts features that are normalized, reduced via Principal Component Analysis, and encoded into an 8-qubit variational quantum circuit (HybridQNet). The proposed model achieves 96.26% validation and 95.12% test accuracy, demonstrating an efficient, scalable approach for biomedical image analysis.
Accurate classification of blood cells is crucial for early diagnosis and monitoring of hematological disorders. However, manual microscopy is time-consuming, subjective, and prone to inter-observer variability. Deep learning has shown potential in automating this task, but its high computational demands limit its use in resource-constrained settings. This study aims to develop an efficient, hybrid quantum-classical model for blood cell classification using quantum simulation to reduce computational complexity without compromising accuracy. We propose a hybrid framework that integrates a 6-qubit variational quantum circuit (VQC) with a classical EfficientNet-B0 model. The model was trained and tested on the full BloodMNIST dataset, which includes 17,092 microscopic images across eight blood cell types. The proposed model achieved a test accuracy of 96.58% and a validation accuracy of 96.90%. F1-scores ranged from 91.97% to 99.84%, with notable results in neutrophils (98.80%) and lymphocytes (98.36%). These findings confirm the feasibility of combining quantum and classical approaches for medical image classification. The proposed hybrid model shows strong promise for practical use in diagnostic systems, especially in low-resource settings, and offers a pathway for future research in quantum-enhanced medical imaging.
Medical image classification is a critical component of modern healthcare; however, accurate diagnosis remains challenging due to limited annotated datasets, class imbalance, and the high dimensionality of medical imaging data. To address these challenges, a hybrid quantum-classical neural network (HQCNN) is proposed, integrating classical deep learning with variational quantum learning for medical image classification. The proposed architecture combines a five-layer convolutional neural network (CNN) for hierarchical feature extraction with a lightweight 4-qubit variational quantum circuit (VQC) incorporating quantum state encoding, superposition and entanglement mechanisms, and a quantum attention-Fourier (QAF) module. This hybrid design aims to improve nonlinear feature representation and quantum parameter efficiency while maintaining a shallow quantum circuit suitable for noisy intermediate-scale quantum (NISQ)-era constraints. Experimental evaluation on six MedMNIST benchmark datasets demonstrated competitive performance across both binary and multi-class classification tasks. HQCNN achieved 98.88% accuracy on the binary subset of PathMNIST (classes 0 vs. 1), 97.61% accuracy on the multi-class OrganAMNIST dataset, and 86.29% accuracy on BreastMNIST. Comparative experiments and statistical analyses demonstrated consistent improvements over the controlled BHQNN baseline, while component-wise ablation studies showed that the QAF module, superposition and entanglement mechanisms, and expressive parameterized rotations contributed to classification performance in a complementary and dataset-dependent manner. Moreover, HQCNN reduced the number of trainable quantum parameters by approximately 55.6% compared with the baseline hybrid quantum neural network (BHQNN). Noise-aware simulations further showed that the model retained relatively stable predictive performance under moderate depolarizing noise, supporting further evaluation under near-term quantum computing conditions. Overall, the results demonstrate that HQCNN provides a parameter-efficient hybrid quantum-classical framework for medical image classification and offers a promising foundation for further investigation of quantum-enhanced medical image analysis.
Accurate detection and classification of WBC is essential haematological diagnosis, especially to detect the development of lymphoblastic leukaemia.Class imbalance, limited interpretability, and high computational demands are always prevalent issues in classical machine and deep learning.In this paper, a variational quantum classifer (VQC) based hybrid quantum-classical approach to five-class WBC subtype identification (Basophil, Eosinophil, Lymphocyte, Monocyte, Neutrophil) and four-stage detection of lymphoblastic leukaemia (Benign, Early, Pre-leukaemic, Pro-leukaemic) is proposed.The proposed pipeline combines the use of CNN based feature extraction, PCA dimensionality reduction and amplitude encoding within a 3-qubit quantum system parameterised by multi-layered RY rotations, controlled-Z (CZ) and controlled-phase (CPHASE) gates, with optimisation performed using the AdamW algorithm with early stopping.The approaches used to cope with class imbalance are SMOTE and inverse-frequency weighted cross-entropy loss.Explanation is given using SHAP DeepExplainer and gradient based saliency map.The model classifies WBC into 4 categories with an accuracy of 96.8% on macro-averaging and detects the stage of a disease with an accuracy of 95.4% while having only 25-30 trainable parameters, outperforming classical classifiers such as SVM, Random Forest, standard CNN and MobileNetV2.The results show that quantum inspired methods provide a promising route to scalable, parameter-efficient, and interpretable biomedical image analysis.
Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can challenge conventional deep learning models. As such, we investigate in this work the potential of Hybrid Quantum-Classical Neural Networks (HQNNs) to enhance feature representation and improve classification performance in this domain. We propose a modular architecture combining a pre-trained ResNet-50 backbone with a low-dimensional latent bottleneck and a variational quantum circuit, enabling a direct comparison between quantum-enhanced and purely classical transformation mechanisms. To isolate the contribution of the quantum component, we evaluate three architectures: a HQNN model, a Classical Matched Model with an additional nonlinear transformation layer of comparable capacity, and a baseline model without an intermediate transformation stage. Experiments conducted on two publicly available blood cell datasets, namely the Blood Cell Images dataset and the PBC dataset, demonstrate that HQNNs consistently achieve superior or more balanced performance across evaluation metrics. In the Blood Cell Images Dataset, the proposed approach improves macro F1-score by up to 3.7% compared to classical baselines, while improving the F1-score from 98.54% to 98.69% in the more challenging 8-class scenario with near-saturated performance. Additional evaluation on IBM quantum hardware shows that the model remains robust under noise, with only a modest performance degradation relative to simulated results. These results indicate that quantum feature transformations can enhance discriminative representations, particularly in challenging classification scenarios, and highlight the practical potential of HQNN models for medical imaging tasks.
Leukemia is a critical blood-related health problem for which early diagnosis is a key aspect in the survival of the patient. Computer-assisted diagnosis using microscopic white blood cell images has received significant attention because the conventional manual approach is too time-consuming. In this paper, a hybrid Quantum Machine Learning-based approach for the classification of leukemia is presented. In the first place, the microscopic white blood cell images are preprocessed using resizing, normalization, and denoising techniques. Then, Principal Component Analysis is applied for dimensionality reduction. The reduced feature vectors are then mapped onto a quantum system using a Variational Quantum Circuit-based Quantum Neural Network.The presented method also validates the possibility of quantum-assisted learning in biomedical image diagnosis. Additionally, it offers a glimpse of how well a hybrid model would perform under Noisy Intermediate-Scale Quantum (NISQ) conditions. The experimental results are achieved using standard performance parameters such as accuracy, precision, recall, and F1-score. This method indicates how a hybrid model of QML can perform in a computationally simpler manner. This paper also indicates the potential of a quantum-classical system in future clinical applications for leukemia screening and diagnosis.
Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.