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

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          Abstract

          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 stained images by step by step refinement of segmentation and classification stages. Krill Herd Algorithm-based localized active contour model precisely segments cell nuclei from background stroma. A deep belief network based multi-classifier system classifies the labeled cells into mitotic and nonmitotic groups. The proposed method has been evaluated on MITOS data set 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 with other state–of–the-art techniques with average F-score of 84.29% for the MITOS data set and 75% for the clinical data set from RCC.

          Abstract

          This paper presents a multi-classifier system for automatic mitosis detection in breast histopathology images using deep belief networks.

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          Selection of relevant features and examples in machine learning

          Avrim Blum, Pat Langley (1997)
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            A survey of thresholding techniques

            P.K. Sahoo, S M Amin Khalifeh Soltani, A.K.C. Wong (1988)
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              Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition.

              Chengjun Liu, Harry Wechsler (2002)
              This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.
              Article 0 comments Cited 154 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Nair Madhu S. Nair Madhu S.
                Journal
                Journal ID (nlm-ta): IEEE J Transl Eng Health Med
                Journal ID (iso-abbrev): IEEE J Transl Eng Health Med
                Journal ID (publisher-id): 0063400
                Journal ID (publisher-id): JTEHM
                Journal ID (coden): IJTEBN
                Title: IEEE Journal of Translational Engineering in Health and Medicine
                Publisher: IEEE
                ISSN (Electronic): 2168-2372
                Publication date Collection: 2017
                Publication date (Electronic): 25 April 2017
                Volume: 5
                Electronic Location Identifier: 4300211
                Affiliations
                [1] departmentElectrical and Electronics Department, institutionThangal Kunju Musaliar College of Engineering; Kollam691005India
                [2] departmentElectrical Engineering Department, institutionCollege of Engineering Trivandrum (CET); Thiruvananthapuram695016India
                [3] departmentDepartment of Computer Science, institutionUniversity of Kerala; Thiruvananthapuram695581India
                Author notes
                Article
                Publisher ID: 4300211
                DOI: 10.1109/JTEHM.2017.2694004
                PMC ID: 5480254
                PubMed ID: 29018640
                SO-VID: 24f2bf4b-a5ae-4ce4-b44b-ebc51516d480
                Copyright © 2168-2372 © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
                History
                Date received : 12 December 2016
                Date revision received : 09 March 2017
                Date accepted : 06 April 2017
                Date: 04 May 2017
                Page count
                Figures: 9, Tables: 4, Equations: 139, References: 48, Pages: 11
                Categories
                Subject: Article

                Keywords: breast histopathology,mitosis,support vector machine,random forest,multi-classifier system,deep belief networks
                Data availability:
                Keywords: breast histopathology, mitosis, support vector machine, random forest, multi-classifier system, deep belief networks

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