Digital pathology and microscopy image analysis is widely used for comprehensive studies
of cell morphology or tissue structure. Manual assessment is labor intensive and prone to …

This study aims to apply Mask Regional Convolutional Neural Network (Mask R-CNN) to
cervical cancer screening using pap smear histological slides. Based on our current …

Traditional cell classification methods generally extract multiple features of the cell manually.
Moreover, the simple use of artificial feature extraction methods has low universality. For …

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a
highly effective cell imaging based cancer detection tool, where cells are partitioned into …

Computer-assisted diagnosis is key for scaling up cervical cancer screening. However,
current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to …

Classification of cervical cells in Pap smear images is a challenging task due to the
limitations these images exhibit and the complexity of the morphological changes in the …

Traditional screening of cervical cells largely depends on the experience of pathologists,
which also has the problem of low accuracy and poor efficiency. Medical image processing …

Background and objectives The present study proposes an intelligent system for automatic
categorization of Pap smear images to detect cervical dysplasia, which has been an open …

Cervical cancer is the fourth most prevalent disease in women. Accurate and timely cancer
detection can save lives. Automatic and reliable cervical cancer detection methods can be …

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality
among women. In this study, we proposed a novel framework based on Faster RCNN-FPN …