  It has been shown that for automated PAP-smear image classification, nucleus
features can be very informative. Therefore, the primary step for automated
screening can be cell-nuclei detection followed by segmentation of nuclei in
the resulting single cell PAP-smear images. We propose a patch based approach
using CNN for segmentation of nuclei in single cell images. We then pose the
question of ion of segmentation for classification using representation
learning with CNN, and whether low-level CNN features may be useful for
classification. We suggest a CNN-based feature level analysis and a transfer
learning based approach for classification using both segmented as well full
single cell images. We also propose a decision-tree based approach for
classification. Experimental results demonstrate the effectiveness of the
proposed algorithms individually (with low-level CNN features), and
simultaneously proving the sufficiency of cell-nuclei detection (rather than
accurate segmentation) for classification. Thus, we propose a system for
analysis of multi-cell PAP-smear images consisting of a simple nuclei detection
algorithm followed by classification using transfer learning.
