Abstract Background and Objective Early diagnosis and classification of a cancer type can
help facilitate the subsequent clinical management of the patient. Cervical cancer ranks as …

Cervical cancer is the one of the most common cancers in women worldwide, affecting
around 570,000 new patients each year. Although there have been great improvements …

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 …

In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based
method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically …

Accurate segmentation of cervical cells in Pap smear images is an important step in
automatic pre-cancer identification in the uterine cervix. One of the major segmentation …

Cervical cancer is one of the leading causes of cancer death in females worldwide. The
disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier …

In this paper, we present an improved algorithm for the segmentation of cytoplasm and
nuclei from clumps of overlap** cervical cells. This problem is notoriously difficult because …

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 …

The Pap smear test is a manual screening procedure that is used to detect precancerous
changes in cervical cells based on color and shape properties of their nuclei and …

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 …