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 | |
| 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 | China |
| City | Hangzhou |
| Period | 15/7/28 → 15/7/30 |
Fingerprint
Keywords
- Automatic classification
- Gastric biopsy
- Image processing
ASJC Scopus subject areas
- Control and Systems Engineering
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
}
TY - GEN
T1 - Automatic classification of neoplastic lesion on gastric biopsy images
AU - Morotomi, Emi
AU - Tanaka, Toshiyuki
PY - 2015/9/30
Y1 - 2015/9/30
N2 - 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.
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.
KW - Automatic classification
KW - Gastric biopsy
KW - Image processing
UR - http://www.scopus.com/inward/record.url?scp=84960097304&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960097304&partnerID=8YFLogxK
U2 - 10.1109/SICE.2015.7285506
DO - 10.1109/SICE.2015.7285506
M3 - Conference contribution
AN - SCOPUS:84960097304
SN - 9784907764487
SP - 60
EP - 63
BT - 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -