Abstract
Colorectal cancer (CRC) is one of the most prevalent cancer types, but until now only few studies have addressed the problem of automatic recognition of distinct tissue types in histological CRC images. In this paper, we employ two deep convolutional neural networks, the original 50-layer residual network ResNet-50 and a modified residual network ResNet-50-fla-drop, to automatically perform eight-class tissue separation in histological CRC images. Experimental results showed that the proposed modified residual network ResNet-50-fla-drop can achieve a classification accuracy of 94.4% for eight-class CRC tissue separation, and it outperforms other published machine learning methods, including the radial-basis function SVM and VGG-VD-16.
Original language | English |
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Title of host publication | 2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 299-300 |
Number of pages | 2 |
ISBN (Electronic) | 9781728135755 |
DOIs | |
State | Published - 10 2019 |
Externally published | Yes |
Event | 8th IEEE Global Conference on Consumer Electronics, GCCE 2019 - Osaka, Japan Duration: 15 10 2019 → 18 10 2019 |
Publication series
Name | 2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019 |
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Conference
Conference | 8th IEEE Global Conference on Consumer Electronics, GCCE 2019 |
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Country/Territory | Japan |
City | Osaka |
Period | 15/10/19 → 18/10/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Colorectal cancer (CRC)
- Deep neural network
- Histological image
- Residual network