Non-Hodgkin Lymphoma (NHL) represents a significant portion of cancer cases worldwide, with traditional detection methods often lacking in early and precise diagnosis. Leveraging Quantum Machine Learning (QML), we introduce a pioneering approach for detecting NHL using histopathological images. Our methodology integrates a Convolutional Neural Network (CNN) for initial feature extraction, followed by a Quantum Convolutional Neural Network (QCNN) and a Quantum Neural Network (QNN) for enhanced detection capabilities. This novel approach aims to improve diagnostic accuracy and efficiency, potentially leading to better treatment outcomes and reduced healthcare costs.
- Non-Hodgkin Lymphoma
- Quantum Machine Learning
- Quantum Convolutional Neural Network
- Quantum Neural Network
- Histopathological Images
- Utilize CNN for initial feature extraction and QCNN for advanced feature representation.
- Implement QNN for accurate NHL detection, comparing its efficacy against traditional methods.
Our solution follows a comprehensive workflow:
- Data Acquisition: Compile a dataset comprising histopathological images and patient records.
- Feature Extraction: Employ CNN for initial extraction, then enhance features with QCNN.
- Detection: Leverage a QNN for final detection, aiming for higher accuracy and efficiency.
- Patient Privacy: Ensure confidentiality through data de-identification techniques.
By applying QML to NHL detection, we aim to enhance diagnostic processes significantly. This research could serve as a catalyst for broader advancements in precision medicine and early disease detection, potentially improving patient outcomes and reducing treatment costs.
The project utilizes the PCam dataset for training the QML model, available here: PCam Dataset on Kaggle
We have achieved an initial accuracy of 85.56% in NHL detection using our quantum-enhanced system, marking a promising step towards revolutionizing cancer diagnostics.
MIT License
Copyright (c) 2024 Siddharth Bhetariya
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This project is build during the Unisys Innovation Program Y-15
