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IEEE/ICACT20240372 Slide.03        [Big Slide]	Oral Presentation
	Introduction While using Computer-aided diagnosis (CAD) and machine learning methods, cancer datasets often exhibit characteristics such as class imbalance, noisy labels, and complex interactions between features, further complicating accurate diagnosis. Overcoming these challenges enables a growing need for advanced machine-learning techniques in cancer diagnosis By leveraging computational power and advanced algorithms, machine-learning approaches can improve the sensitivity and specificity of cancer diagnosis, enhance risk stratification, and aid in treatment decision-making.

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