Automatic classification of neoplastic lesion on gastric biopsy images

Emi Morotomi; Toshiyuki Tanaka

doi:10.1109/SICE.2015.7285506

Automatic classification of neoplastic lesion on gastric biopsy images

Emi Morotomi, Toshiyuki Tanaka

Department of Applied Physics and Physico-Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In histopathological diagnosis, pathologists observe the biopsy images and diagnose the tumor grade. However, the number of pathologists has been decreasing, so the demand for cancer diagnosis support system has been increasing in recent years. Therefore, this study proposes the method for automatic classification to two classes which are neoplastic lesion, and non-neoplastic lesion. Our method consists of image inputting, region extraction, feature calculation, and discriminant analysis. As the result, our method showed 93.33% accuracy on the neoplastic lesion, and 82.86% accuracy on the non-neoplastic lesion.

Original language	English
Title of host publication	2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	60-63
Number of pages	4
ISBN (Print)	9784907764487
DOIs	https://doi.org/10.1109/SICE.2015.7285506
Publication status	Published - 2015 Sep 30
Event	54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015 - Hangzhou, China Duration: 2015 Jul 28 → 2015 Jul 30

Other

Other	54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015
Country/Territory	China
City	Hangzhou
Period	15/7/28 → 15/7/30

Keywords

Automatic classification
Gastric biopsy
Image processing

ASJC Scopus subject areas

Control and Systems Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/SICE.2015.7285506

Cite this

Automatic classification of neoplastic lesion on gastric biopsy images. / Morotomi, Emi; Tanaka, Toshiyuki.

2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 60-63 7285506.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Morotomi, E & Tanaka, T 2015, Automatic classification of neoplastic lesion on gastric biopsy images. in 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015., 7285506, Institute of Electrical and Electronics Engineers Inc., pp. 60-63, 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015, Hangzhou, China, 15/7/28. https://doi.org/10.1109/SICE.2015.7285506

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abstract = "In histopathological diagnosis, pathologists observe the biopsy images and diagnose the tumor grade. However, the number of pathologists has been decreasing, so the demand for cancer diagnosis support system has been increasing in recent years. Therefore, this study proposes the method for automatic classification to two classes which are neoplastic lesion, and non-neoplastic lesion. Our method consists of image inputting, region extraction, feature calculation, and discriminant analysis. As the result, our method showed 93.33% accuracy on the neoplastic lesion, and 82.86% accuracy on the non-neoplastic lesion.",

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AB - In histopathological diagnosis, pathologists observe the biopsy images and diagnose the tumor grade. However, the number of pathologists has been decreasing, so the demand for cancer diagnosis support system has been increasing in recent years. Therefore, this study proposes the method for automatic classification to two classes which are neoplastic lesion, and non-neoplastic lesion. Our method consists of image inputting, region extraction, feature calculation, and discriminant analysis. As the result, our method showed 93.33% accuracy on the neoplastic lesion, and 82.86% accuracy on the non-neoplastic lesion.

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Automatic classification of neoplastic lesion on gastric biopsy images

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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