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Automatic Nuclei Detection in Histopathological Images based on Convolutional Neural Networks

Topics: Medical Image Acquisition, Processing, Analysis, and Detection; Medical Signal Acquisition, Analysis and Processing; Pattern Recognition & Machine Learning for Biosignal Data; Processing and Analysis of Image and Video Data

Authors: Roaa Alah 1 ; Gokhan Bilgin 2 and Abdulkadir Albayrak 2

Affiliations: 1 Dpt. of Computer Engineering, Yildiz Technical University, 34220 Istanbul and Turkey ; 2 Dpt. of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey, Signal and Image Processing Laboratory (SIMPLAB), Yildiz Technical University, 34220 Istanbul and Turkey

ISBN: 978-989-758-353-7

Keyword(s): Nuclei Detection, Histopathological Images, FCM Algorithm, Convolution Neural Networks, Deep Learning.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing

Abstract: Analysis of cells in histopathological images with conventional manual methods is relatively expensive and time-consuming work for pathologists. Recently, computer aided and facilitated researches for the diagnostic algorithms have obtained a high significance to assist the pathologists to extract cellular structures. In this paper, we are compering the conventional fuzzy c-means (FCM) clustering method with the proposed automated detection system based on Tiny-Convolutional Neural Network (Tiny-CNN) to detect center of nucleus in histopathological images, Also, in this study, we are tried to find center of nucleus by combined unsupervised method (FCM) with supervised method (Tiny-CNN). Briefly, First step, nuclei centers are detected with FCM algorithm which is applied as a clustering-segmentation method to perform segmentation of nucleus cellular and nucleus non-cellular structure to find the correct center of nuclei. Second step, the deep learning method is used to detect center of nucleus based automated method. Afterward, combined each of these individual methods to evaluate our model for extracting the center of nucleus on two different data set the University of California Santa Barbara’s UCSB-58 data set and data set University of Warwick’s CRC-100 data set. (More)

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Paper citation in several formats:
Alah, R.; Bilgin, G. and Albayrak, A. (2019). Automatic Nuclei Detection in Histopathological Images based on Convolutional Neural Networks.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 BIOSIGNALS: BIOSIGNALS, ISBN 978-989-758-353-7, pages 193-200. DOI: 10.5220/0007484301930200

@conference{biosignals19,
author={Roaa Safi Abed Alah. and Gokhan Bilgin. and Abdulkadir Albayrak.},
title={Automatic Nuclei Detection in Histopathological Images based on Convolutional Neural Networks},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 BIOSIGNALS: BIOSIGNALS,},
year={2019},
pages={193-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007484301930200},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 BIOSIGNALS: BIOSIGNALS,
TI - Automatic Nuclei Detection in Histopathological Images based on Convolutional Neural Networks
SN - 978-989-758-353-7
AU - Alah, R.
AU - Bilgin, G.
AU - Albayrak, A.
PY - 2019
SP - 193
EP - 200
DO - 10.5220/0007484301930200

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