Abstract
The well-known Group Classification method for hematoxylin and eojin stained gastric tumors uses morphological features of histology patterns within a tissue slide to classify it into 5 grades from Group1 to Group5. Our approach developed an automated classification method being used for automated Group Classification of gastric tumor images. We have demonstrate the performance of the proposed method for a three class classification ± Group1 (benign), Group3 (gastric adenoma), Group5 (gastric cancer) ± on a 90 teaching dataset and 90 test dataset using Support Vector Machine and achieved accuracy of 75.6% on Group1, 64.4% on Group3, and 95.6% on Group5. Our approach combines the morphological features such as nuclear-cytoplasmic ratio, some texture features, and HLAC (higher order local autocorrelation).
| Original language | English |
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| Pages | 2502-2506 |
| Number of pages | 5 |
| Publication status | Published - 2013 |
| Event | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan Duration: 2013 Sept 14 → 2013 Sept 17 |
Other
| Other | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 |
|---|---|
| Country/Territory | Japan |
| City | Nagoya |
| Period | 13/9/14 → 13/9/17 |
Keywords
- Automated classification
- Digital pathology
- Gastric biopsy
- Image processing
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
