This paper investigates quantum image processing for histopathological cancer detection, focusing on enhanced feature extraction and improved classification. We propose a hybrid quantum-classical framework in which quantum preprocessing circuits, specifically, Quantum Hadamard Edge Detection (QHED) and the Quantum Fourier Transform (QFT), extract subtle spatial and spectral features from hematoxylin and eosin-stained whole-slide images. These quantum-enhanced features are then classified using deep neural networks such as ResNet-50, VGG-16, and UNet. Experiments on the PatchCamelyon dataset demonstrate that quantum-enhanced preprocessing increases sensitivity in detecting cancerous regions, while integrating hybrid preprocessing with an SVM classifier further improves classification accuracy. Although quantum noise remains a challenge, our results highlight the promise of quantum image pre-processing and feature extraction for advancing histopathological image classification.
This paper investigates quantum image processing for histopathological cancer detection, focusing on enhanced feature extraction and improved classification. We propose a hybrid quantum-classical framework in which quantum preprocessing circuits, specifically, Quantum Hadamard Edge Detection (QHED) and the Quantum Fourier Transform (QFT), extract subtle spatial and spectral features from hematoxylin and eosin-stained whole-slide images. These quantum-enhanced features are then classified using deep neural networks such as ResNet-50, VGG-16, and UNet. Experiments on the PatchCamelyon dataset demonstrate that quantum-enhanced preprocessing increases sensitivity in detecting cancerous regions, while integrating hybrid preprocessing with an SVM classifier further improves classification accuracy. Although quantum noise remains a challenge, our results highlight the promise of quantum image pre-processing and feature extraction for advancing histopathological image classification.
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.
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.
Cancer remains one of the leading causes of mortality worldwide, making early and accurate detection a critical challenge in medicine. Recent advances in quantum computing have opened new avenues for enhancing machine learning techniques in medical imaging and diagnosis. In this work, we apply quantum machine learning models, including Quantum Convolutional Neural Networks (QCNN), to the task of breast cancer detection using ultrasound images. We designed and implemented a hybrid pipeline combining classical preprocessing techniques with quantum-enhanced classification. Our experiments were conducted on real medical imaging datasets, and the results demonstrate the potential of quantum approaches to improve diagnostic accuracy over classical baselines. This research lies at the intersection of quantum computing, artificial intelligence, and medical image analysis, and highlights the promise of quantum-enhanced methods in addressing complex healthcare problems.
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.
The digitization of histopathology slides into high-resolution Whole-Slide Images (WSIs) has created a computational bottleneck for classical image processing algorithms. Tasks such as feature extraction and tumor segmentation require immense classical memory and processing power. This paper explores the application of Quantum Image Processing (QIP) as a solution. By utilizing frameworks such as the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR), we theoretically demonstrate how multi-gigapixel tissue scans can be encoded into high-dimensional Hilbert spaces. We propose a hybrid quantum-classical architecture that performs quantum edge detection via the Quantum Fourier Transform (QFT), achieving an exponential reduction in computational time complexity compared to classical convolutional edge filters. The results establish a foundational methodology for integrating quantum algorithms into clinical digital pathology workflows.
Breast cancer is one among the most common diseases that are encountered in the world and one of the principal causes of death among women, early detection being crucial to enhance survival rates but mammograms are an important tool that can save lives by finding breast cancer at its earliest stages but the manual evaluation is dependent on a radiologist's clear interpretation and analysis of which sometimes erroneously or misses the key findings. This research work deals with breast cancer detection from mammogram images and hence provides a solution for the issues identified by training Quantum Convolutional Neural Network (Quantum CNN). Quantum CNNs is recently being integrated with classical deep learning models in the form of Quantum CNN where quantum mechanical properties are used to support feature extraction and data handling. This research work trains the Quantum CNN using publicly available datasets, including the CBIS-DDSM Breast Cancer Image Dataset and the Breast Histopathology Images dataset. Key methods used include data preprocessing techniques such as Region of Interest (ROI) extraction, morphological operations, and quantum-enhanced convolutional layers for image classification. The proposed model demonstrates improved accuracy and computational efficiency over classical CNNs, making it a promising tool for more accurate and faster breast cancer detection.