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| Main Authors: | , , , |
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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19186120 |
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Table of Contents:
- <p><strong>Abstract</strong></p> <p>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 <em>“Leukemia Detection using Quantum Machine Learning”</em>, which integrates classical image processing with quantum computing techniques.</p> <p>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.</p> <p>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.</p> <p>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.</p>