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IEEE/ICACT20240384 Slide.04        [Big Slide]	Oral Presentation
	our objective is to develop a comprehensive understanding of the factors influencing accurate axillary lymph-node (ALN) assessment in breast cancer patients. We aim to enhance the predictive accuracy of ALN status by employing advanced methodologies, with a specific focus on reducing false-negative rates associated with current diagnostic approaches. To achieve this, we are leveraging state-of-the-art machine learning algorithms to analyze complex datasets, allowing for a more nuanced evaluation of breast cancer progression. The study aims to provide clinicians with a robust decision support tool.

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