Impact of Input Resolution in Mammographic Mass Detection: A Controlled Multi-Architecture Study

This controlled study evaluates how input image resolution affects mammographic mass detection performance across multiple deep learning architectures, providing empirical evidence on a key preprocessing decision in medical imaging AI. The research quantifies resolution-performance tradeoffs relevant to deployment decisions in clinical mammography screening systems.

This controlled study evaluates how input image resolution affects mammographic mass detection performance across multiple deep learning architectures, providing empirical evidence on a key preprocessing decision in medical imaging AI. The research quantifies resolution-performance tradeoffs relevant to deployment decisions in clinical mammography screening systems. Findings directly inform input specification choices for regulated medical AI systems in healthcare organizations.

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