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SotA/Medical/Medical Image Classification

Medical Image Classification

20 benchmarks424 papers

Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.

Benchmarks

Medical Image Classification on NCT-CRC-HE-100K

Accuracy (%)F1-ScorePrecisionSpecificity

Medical Image Classification on Chest X-Ray14 2% labeled

AUC

Medical Image Classification on ImageNet

GFLOPsTop 1 Accuracy

Medical Image Classification on PCOS Classification

1:1 Accuracy

Medical Image Classification on COVIDGR

Accuracy

Medical Image Classification on CheXphoto

Mean AUC

Medical Image Classification on Galaxy10 DECals

Top-1 Accuracy (%)

Medical Image Classification on IDRiD

AccuracyAccuracy (% )

Medical Image Classification on ISIC 2017

Accuracy

Medical Image Classification on ISIC 2020 Challenge Dataset

AUC

Medical Image Classification on Malaria Dataset

F1 score

Medical Image Classification on OASIS 3

SensitivitySpecificityAUCAccuracy