EARLY BREAST CANCER DETECTION USING ENSEMBLE QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN)

Abstract

This invention introduces a QCNN-based system for early breast cancer detection, utilizing quantum computing principles to improve image processing and diagnostic accuracy. By integrating transfer learning and cloud infrastructure, the system reduces data requirements and computational demands, offering an accessible and effective diagnostic tool for breast cancer.

Specification

Description:FIELD OF THE INVENTION
This invention relates to medical diagnostics and machine learning, specifically an Ensemble Quantum Convolutional Neural Network (QCNN) system designed for the early detection of breast cancer. Integrating advanced quantum computing principles with convolutional neural networks, this system offers high accuracy in detecting cancerous markers in X-ray images while reducing computational demands.
BACKGROUND OF THE INVENTION
Breast cancer is one of the most common and deadly diseases among women worldwide, contributing to approximately 9 million deaths annually. Early detection is crucial to successful treatment outcomes; however, current diagnostic methods face challenges such as high computational requirements, slow processing times, and the need for extensive datasets for accurate results. Traditional machine learning models often require significant fine-tuning, large amounts of data, and substantial computational power, limiting their scalability in medical diagnostics.
This invention addresses these limitations by using an Ensemble Quantum Machine Learning (QML) approach, which combines quantum computing principles with deep learning models for breast cancer detection. Unlike traditional systems, this quantum-enhanced method requires less data and computing power while delivering higher accuracy, making it feasible for early-stage detection. The Ensemble QCNN model significantly advances the efficiency of cancer detection, leveraging quantum properties to optimize feature extraction, pattern recognition, and diagnostic accuracy.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention introduces an Ensemble Quantum Convolutional Neural Network (QCNN) system for early breast cancer detection. This model processes breast cancer X-ray images using Ensemble Quantum principles within a convolutional network, enhancing computational efficiency and reducing data requirements. The system's architecture incorporates multicore CPU or GPU units, a cloud-based data repository, and a network infrastructure that transmits processed data to mobile devices or healthcare providers. Designed to improve diagnostic speed and accuracy, this model offers a low-computation solution for early cancer detection.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The Ensemble Quantum Convolutional Neural Network (QCNN) system comprises several essential components designed for optimal early detection of breast cancer. The system begins with the acquisition of breast cancer X-ray images, which are stored and processed in a cloud-based infrastructure. These images serve as input data, which undergo pre-processing and are subsequently analyzed by the QCNN model.
The QCNN model architecture combines classical convolutional neural networks (CNN) with Ensemble Quantum properties to enhance processing efficiency. Quantum computing concepts, such as superposition and entanglement, are applied within convolutional layers to handle complex data and improve feature extraction. The integration of these quantum principles into CNN layers allows for better pattern recognition in X-ray images, enabling the identification of subtle cancerous markers that traditional models might overlook.
The Ensemble Quantum Transfer Learning module further enhances diagnostic accuracy by using ResNet-18 as a base model for feature extraction. This technique employs transfer learning to adapt pre-trained models for quantum-enhanced diagnostics, allowing the QCNN system to classify images as either cancerous or non-cancerous effectively. The transfer learning approach reduces the need for large datasets, making the system efficient in handling smaller datasets without compromising diagnostic accuracy.
The infrastructure of the QCNN system includes a multicore CPU or GPU setup, enabling high-performance computing capabilities necessary for handling complex quantum calculations. The data processing occurs in a Linux-based environment with machine learning libraries like Keras for CNN modeling and PennyLane for Quantum Machine Learning integration. The network infrastructure, facilitated by Wi-Fi, supports real-time data transmission between the cloud and end-user devices, such as mobile phones and computers, ensuring timely and accessible diagnostic results.
The data storage component of the QCNN system is designed to securely handle patient data through cloud storage and relational or NoSQL databases. This storage setup supports the system's data requirements, allowing the secure and efficient management of medical images and diagnostic results.
The advantages of the Ensemble QCNN system in breast cancer diagnostics include reduced computational requirements, faster processing times, and enhanced diagnostic accuracy. By leveraging the parallelism of quantum computing, the QCNN system can perform simultaneous data processing, which is particularly beneficial in handling high-dimensional medical imaging data. Additionally, the resilience of quantum algorithms to noise and data irregularities improves model robustness, making the system reliable even with noisy medical datasets.
, Claims:1. An early breast cancer detection system using an Ensemble Quantum Convolutional Neural Network (QCNN), comprising quantum-enhanced convolutional layers for efficient image processing and diagnosis.
2. The system as claimed in Claim 1, wherein the QCNN model utilizes quantum computing principles such as superposition and entanglement to optimize feature extraction and pattern recognition in X-ray images.
3. The system as claimed in Claim 1, wherein an Ensemble Quantum Transfer Learning module with ResNet-18 is used to improve image classification accuracy and reduce data requirements.
4. The system as claimed in Claim 1, wherein the multicore CPU or GPU infrastructure supports high-performance computing for complex quantum calculations within the diagnostic process.
5. The system as claimed in Claim 1, wherein the cloud-based data storage and network infrastructure enable real-time data processing and transmission to mobile devices and computers for accessible diagnostics.
6. The system as claimed in Claim 1, wherein machine learning libraries like Keras and PennyLane integrate with Linux-based operating systems to support QCNN model training and deployment.
7. The system as claimed in Claim 1, wherein data storage is managed securely through relational, NoSQL, or cloud databases, ensuring data privacy and efficient access to patient diagnostic information.
8. A method for early breast cancer detection as claimed in Claim 1, involving Ensemble Quantum Machine Learning techniques for real-time image classification and secure data transmission.
9. The system as claimed in Claim 1, wherein it enhances diagnostic accuracy through quantum parallelism, enabling simultaneous data processing and efficient cancer marker identification.
10. The system as claimed in Claim 1, wherein it provides a low-computation, high-accuracy solution for early cancer detection, catering to medical diagnostics with limited data and computational resources.

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