A Multi-Classifier System for Automatic Mitosis Detection in Breast Histopathology Images using Deep Belief Networks

April 27, 2017

Sabeena Beevi K, Madhu S. Nair, Bindu G. R.

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

Abstract

A Multi-Classifier System for Automatic Mitosis Detection in Breast Histopathology Images using Deep Belief Networks

Mitotic count is an important diagnostic factor in breast cancer grading and prognosis. Detection of mitosis in breast histopathology images is very challenging mainly due to diffused intensities along object boundary and shape variation in different stages of mitosis. This paper demonstrates an accurate technique for detecting the mitotic cells in Hematoxyline and Eosin (H & E) stained images by step by step refinement of segmentation and classification stages. Krill Herd Algorithm (KHA) based Localized Active Contour Model precisely segments cell nuclei from background stroma. A Deep Belief Network based Multi-Classifier System (DBN-MCS) classify the labelled cells into mitotic and nonmitotic groups. The proposed method has been evaluated on MITOS dataset provided for MITOS-ATYPIA contest 2014 and also on clinical images obtained from Regional Cancer Centre (RCC), Thiruvananthapuram, which is a pioneer institute specifically for cancer diagnosis and research in India. The algorithm provides improved performance compared to other state–of–the–art techniques with average F-score of 84.29% for the MITOS dataset and 75% for the clinical data set from RCC.

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