  Liquid-based cytology (LBC) is a reliable automated technique for the
screening of Papanicolaou (Pap) smear data. It is an effective technique for
collecting a majority of the cervical cells and aiding cytopathologists in
locating abnormal cells. Most methods published in the research literature rely
on accurate cell segmentation as a prior, which remains challenging due to a
variety of factors, e.g., stain consistency, presence of clustered cells, etc.
We propose a method for automatic classification of cervical slide images
through generation of labeled cervical patch data and extracting deep
hierarchical features by fine-tuning convolution neural networks, as well as a
novel graph-based cell detection approach for cellular level evaluation. The
results show that the proposed pipeline can classify images of both single cell
and overlapping cells. The VGG-19 model is found to be the best at classifying
the cervical cytology patch data with 95 % accuracy under precision-recall
curve.
