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Cancer Diagnosis Optimization in Medical Imaging Through Machine Learning Feature Selection Algorithms

Khadija ShazlyHakan KhanNajaad OubeBlika
Volume 4

Abstract

The integration of machine learning (ML) and neural network techniques has significantly enhanced cancer diagnosis by optimizing feature selection in medical imaging. Advanced models like D-Cube utilize diffusion models combined with contrastive learning to extract robust hyperfeatures, improving classification performance even in scenarios with data imbalance and limited samples . Similarly, the application of random projection algorithms in conjunction with support vector machines (SVMs) has demonstrated improved accuracy in breast lesion classification by reducing feature dimensionality and enhancing model robustness . These methodologies underscore the critical role of effective feature selection in developing reliable and efficient diagnostic tools, facilitating early detection and personalized treatment planning in oncology. 

Keywords

Machine Learning, Feature Selection, Medical Imaging, Cancer Diagnosis, Neural Networks, Dimensionality Reduction

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