Considerations for a PAP Smear Image Analysis System with CNN Features

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 a...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2018-06
Main Authors: Srishti Gautam, Harinarayan, K K, Jith, Nirmal, Sao, Anil K, Bhavsar, Arnav, Natarajan, Adarsh
Format: Article
Language:English
Subjects:
Algorithms
Automation
Classification
Decision trees
Image analysis
Image classification
Image detection
Image segmentation
Level (quantity)
Machine learning
Nuclei (cytology)
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422