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- W4287514578 abstract "Cervical cancer disproportionately affects low and middle income countries. Automated visual evaluation – using deep learning to analyze a digital cervix photograph – has been proposed for patient management. Image quality remains a key challenge, as it can be degraded by many types of image defects. A series of such defects were artificially added to a test set consisting of N=344 digitized cervigram images from existing studies. Replicate test sets were created for different image defects: blur, recoloring, obstructions of different colors and directions, rotations, and white Gaussian noise. The augmented images were evaluated by a classifier. The two most significant image defects were blur and Gaussian noise." @default.
- W4287514578 created "2022-07-25" @default.
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- W4287514578 date "2022-03-07" @default.
- W4287514578 modified "2023-09-27" @default.
- W4287514578 title "A survey of image quality defects and their effect on the performance of an automated visual evaluation classifier" @default.
- W4287514578 doi "https://doi.org/10.1117/12.2610213" @default.
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