Blood group classification is a critical aspect of medical diagnostics, essential for transfusion medicine, organ transplantation and prenatal care.Traditional serological methods while effective, are time-consuming, require specialized laboratory facilities and are subject to human error.This research introduces a novel quantum-enhanced approach to blood group classification that combines deep learning techniques with quantum machine learning algorithms.Our methodology leverages convolutional neural networks (CNNs) optimized through quantum computing principles to analyze blood sample images for accurate and rapid blood group determination.Using a comprehensive dataset of 10,000 blood sample images across eight blood groups (A+, A-, B+, B-, AB+, AB-, O+, O-), we developed and validated our hybrid quantum-classical model.The proposed quantum-enhanced CNN achieved 99.5% classification accuracy, outperforming traditional serological methods (95.0%) and conventional machine learning approaches such as SVM (97.5%) and Random Forest (98.0%).Processing time was significantly reduced from 300 seconds for traditional methods to just 3.5 seconds for our quantum-enhanced approach.We further evaluated various CNN architectures with ResNet50 demonstrating the highest accuracy (99.0%) among classical implementations.The quantum advantage was particularly evident with larger datasets, showing consistent performance improvements across all testing scenarios.Our approach addresses critical gaps in current blood typing methodologies by providing a non-invasive, rapid and highly accurate alternative that can be deployed in resource-limited settings and emergency situations.This research demonstrates the practical application of quantum computing principles in healthcare diagnostics and establishes a foundation for further exploration of quantum-enhanced medical image analysis [1][2][3] .
Blood group classification is a critical aspect of medical diagnostics, essential for transfusion medicine, organ transplantation and prenatal care.Traditional serological methods while effective, are time-consuming, require specialized laboratory facilities and are subject to human error.This research introduces a novel quantum-enhanced approach to blood group classification that combines deep learning techniques with quantum machine learning algorithms.Our methodology leverages convolutional neural networks (CNNs) optimized through quantum computing principles to analyze blood sample images for accurate and rapid blood group determination.Using a comprehensive dataset of 10,000 blood sample images across eight blood groups (A+, A-, B+, B-, AB+, AB-, O+, O-), we developed and validated our hybrid quantum-classical model.The proposed quantum-enhanced CNN achieved 99.5% classification accuracy, outperforming traditional serological methods (95.0%) and conventional machine learning approaches such as SVM (97.5%) and Random Forest (98.0%).Processing time was significantly reduced from 300 seconds for traditional methods to just 3.5 seconds for our quantum-enhanced approach.We further evaluated various CNN architectures with ResNet50 demonstrating the highest accuracy (99.0%) among classical implementations.The quantum advantage was particularly evident with larger datasets, showing consistent performance improvements across all testing scenarios.Our approach addresses critical gaps in current blood typing methodologies by providing a non-invasive, rapid and highly accurate alternative that can be deployed in resource-limited settings and emergency situations.This research demonstrates the practical application of quantum computing principles in healthcare diagnostics and establishes a foundation for further exploration of quantum-enhanced medical image analysis [1][2][3] .
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.
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.
Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum-classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This is followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness is based on the development of an optimizer with a loss base adaptive momentum. Several algorithms are developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms are tested against a Kaggle brain dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.
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.
Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide, and early detection plays a crucial role in improving survival rates and treatment outcomes.Conventional diagnostic methods such as mammography, ultrasound imaging, and histopathological analysis rely heavily on expert interpretation and classical machine learning techniques.Although classical deep learning models, especially Convolutional Neural Networks (CNNs), have demonstrated impressive performance in medical image analysis, they face inherent limitations related to computational complexity, scalability, and the curse of dimensionality when handling large, high-resolution datasets.With the rapid advancement of quantum computing, Quantum Machine Learning (QML) has emerged as a promising paradigm capable of addressing these challenges by leveraging quantum phenomena such as superposition and entanglement.This research explores the application of Quantum Convolutional Neural Networks (Quantum CNNs or QCNNs) for breast cancer detection, aiming to enhance classification accuracy while reducing computational overhead.The proposed approach integrates quantum circuits with classical preprocessing techniques to extract discriminative features from breast cancer datasets, such as mammographic or histopathological images.By encoding image features into quantum states and performing convolution operations through parameterized quantum circuits, the Quantum CNN model is designed to learn complex patterns more efficiently than classical counterparts.The study evaluates the effectiveness of Quantum CNNs in distinguishing between benign and malignant breast tumors and compares its performance with traditional CNN models.The findings highlight the potential of Quantum CNNs to revolutionize medical image analysis by offering faster convergence, improved generalization, and robustness against noise.This research contributes to the growing field of quantumassisted healthcare systems and demonstrates how quantum computing can be harnessed to address critical challenges in breast cancer diagnosis.
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.