Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images.

Abstract:

:The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of them k-Nearest Neighbor (k-NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design and k-NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem.

journal_name

Biomed Res Int

authors

Joutsijoki H,Haponen M,Rasku J,Aalto-Setälä K,Juhola M

doi

10.1155/2016/3025057

subject

Has Abstract

pub_date

2016-01-01 00:00:00

pages

3025057

eissn

2314-6133

issn

2314-6141

journal_volume

2016

pub_type

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