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.
Cervical cancer disproportionately hurts underserved women from disadvantaged communities. Automated visual evaluation (AVE), which analyzes white light cervical images using machine learning, is being considered for management of screen-positive patients. Gaussian noise was identified as degrading AVE performance. Two noise correction approaches were tested on images from historic data with added Gaussian noise. One denoising method (VDNet) was based on neural networks; the other used conventional Gaussian blur filtering. Images were evaluated by an object detection network (RetinaNet), and by a binary pathology ResNeSt classifier. VDNet filtering limited AVE performance degradation at higher noise levels, while Guassian blur only worked on low noise levels.
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