A multi-resolution CNN with 1.7M parameters achieves 91.28% accuracy on the SIPaKMeD cervical cell dataset, paired with a UNet-based multi-task segmenter, but the claimed progression-risk score is not validated.
Principal components analysis (PCA),
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Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer
A multi-resolution CNN with 1.7M parameters achieves 91.28% accuracy on the SIPaKMeD cervical cell dataset, paired with a UNet-based multi-task segmenter, but the claimed progression-risk score is not validated.