Researchers led by Dina Abdulaziz AlHammadi introduce a novel deep neural network that combines inverted residual structures with self-attention mechanisms for sophisticated medical imaging classification. The 2025 study, featured in Sci Rep, demonstrates improved accuracy and speed in cancer diagnostics. Consider how this framework can streamline imaging analysis to support more informed clinical decisions.

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Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture