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International Conference on Emerging Engineering Technologies and Applications (IC-EETA)

Medical Image Recognition for Breast Cancer Detection Using Hybrid Quantum-Classical Deep Learning

Thomas Samraj Lawrence, Martin Margala, Prąsun Chakrabarti, Siva Shankar S, G. Nagarajan

Published Nov 6, 2025• 0 citations
Publisher

Abstract

One of the common and maybe fatal diseases harming women all around, breast cancer is one such condition. Accurate medical image analysis allows for early detection, which can greatly increase survival rates. Although they encounter challenges such as high computational expenses, poor data efficiency, and suboptimal feature extraction, conventional deep learning methods have produced promising results. By using quantum parallelism and entanglement to enable more quick and more complex computations, quantum computing provides a new approach to overcome these limitations. This paper intends to propose a hybrid quantum-classical deep learning model for the identification of breast cancer areas in mammographic images. The proposed method combines a quantum convolutional neural network (QCNN) with a classical convolutional neural network (CNN) to benefit from the advantages accompanying both classical and quantum processing. The hybrid model processes the mammogram data using classical pre-processing and quantum convolution layers; classification then follows with fully connected layers followed by classical pooling. The results of the MIAS dataset experiments indicate a classification accuracy of 98.4%, which is superior to the operation of traditional CNN and transfer learning-based models.

DOI
10.1109/ic-eeta66496.2025.11548221

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One of the common and maybe fatal diseases harming women all around, breast cancer is one such condition. Accurate medical image analysis allows for early detection, which can greatly increase survival rates. Although they encounter challenges such as high computational expenses, poor data efficiency, and suboptimal feature extraction, conventional deep learning methods have produced promising results. By using quantum parallelism and entanglement to enable more quick and more complex computations, quantum computing provides a new approach to overcome these limitations. This paper intends to propose a hybrid quantum-classical deep learning model for the identification of breast cancer areas in mammographic images. The proposed method combines a quantum convolutional neural network (QCNN) with a classical convolutional neural network (CNN) to benefit from the advantages accompanying both classical and quantum processing. The hybrid model processes the mammogram data using classical pre-processing and quantum convolution layers; classification then follows with fully connected layers followed by classical pooling. The results of the MIAS dataset experiments indicate a classification accuracy of 98.4%, which is superior to the operation of traditional CNN and transfer learning-based models.
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.
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.
Breast cancer is the second most common type of cancer among women worldwide. About 12.9% of women born in the United States today are expected to develop breast cancer during their lifetimes, equating to a chance of 1 in 8. Early and accurate classification of breast cancer is a vital task in medical imaging, as it significantly affects treatment outcomes and patient survival rates. In this work, a hybrid classical-quantum deep learning model that combines quantum transfer learning using ResNet152 with a dressed quantum network employing Hadamard, CNOT, and Ry gates is proposed. This integration takes advantage of the advanced feature extraction capabilities of ResNet152 along with the computational efficiencies and potential advantages offered by quantum networks. Our model achieves a training accuracy of 90.42% and a validation accuracy of 89.90%, demonstrating the effectiveness of incorporating quantum computing into deep learning workflows. In comparison, the classical ResNet152 achieves a training accuracy of 87.23% and validation accuracy of 85.78%, while ResNet50 reaches 86.89% training and 86.76% validation accuracy. These results underscore the potential of hybrid classical-quantum approaches in the advancement of medical diagnostics and highlight a promising direction to improve performance in complex classification tasks.
Experts predict that the use of artificial intelligence and quantum computing will transform medicine including medical imaging. One of the common malignant tumors in women and seriously threatens women’s physical and mental health is breast cancer. The high incidence and mortality of breast cancer are seriously threatening women’s physical and mental health. The long time it took to get breast cancer’s test result and the conditions which make delay without being treated which caused the loss of lives due to latency. Early screening for breast cancer via mammography, ultrasound (US) and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. Artificial intelligence has been extensively researched for breast cancer screening and has demonstrated good performance in image identification tests. This research was introduced analytical estimation for quantum Convolutional Neural Network for breast cancer detection, such as in the identification, segmentation and classification of lesions, breast density assessment and breast cancer risk assessment. Objectives will be to: To detect the breast cancer on a quantum convolutional neural network algorithm using image recognition, to create and develop a CNN and QCNN algorithm that will detect breast Cancer using Deep Learning and to early predict pathological complete response to neoadjuvant chemotherapy and survival analysis using Deep Learning. Data was analyzed through the use of descriptive and deep learning models like CNN and QCNN models. This research used also Google Collab to analyze the data and used data analysis tools python as programming language. From the study results, we have demonstrated the efficacy of quantum convolutional neural networks (QCNNs) to detect breast cancer cells. Using the techniques of deep learning and supervised learning in the quantum framework, we have proposed and tested the effectiveness of quantum CNN on the quantum simulator available at the IBM quantum experience platform.
The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.
Early diagnosis of breast cancer is crucial to combat its severity. Yet, it’s a challenging endeavor to tackle via traditional diagnosis methods. Several machine learning architectures have already been proposed to assist medical experts in diagnosis. However, architectural complexity hinders the goal of developing a cost-effective diagnosis model. Recently, quantum computing has become popular in machine learning due to its potential computational benefits. Although quantum computing has yet to show any significant improvement in machine learning due to its limited resources and complexity, it may reduce the computational complexity of machine learning in the future. Therefore, we proposed a novel hybrid classical-quantum machine learning model for breast cancer prediction. We collected the BreastMNIST dataset of 780 benign and malignant full-image mammogram samples. Our task is to create an efficient breast cancer-classifying hybrid machine learning model. The proposed method employs a modified ResNet50 as a feature extractor and a Quantum Neural Network to train on that feature and generate desired predictions. A Convolutional layer is applied as a dimension-reduction technique for features generated through ResNet50. Our architecture proved superior to other state-of-the-art hybrid methods, achieving an accuracy of 69% against 67%. An in-depth comparative analysis has been performed that shows the novelty of our research. This study paves the path for hybrid classical-quantum machine learning research in medical image classification.