Convolutional Neural Network (CNN) for gland images classification
| Autor: | Toto Haryanto, Heru Suhartanto, Ito Wasito |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science Deep learning Feature extraction Process (computing) Pattern recognition Convolutional neural network 030218 nuclear medicine & medical imaging Convolution 03 medical and health sciences Identification (information) 0302 clinical medicine Feature (computer vision) Medical imaging Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | 2017 11th International Conference on Information & Communication Technology and System (ICTS). |
| DOI: | 10.1109/icts.2017.8265646 |
| Popis: | An automatic detection of histopathological images has an important role in helping diagnose step. Even, for determining the status of cancer, benign or malignant A conventional way in cancer detection has infirmity like user dependency, the tendency to the incorrect identification and takes more time. Convolutional Neural Network (CNN) is one of the deep learning architecture that can accommodate automatic feature extraction and classification directly. The ability of CNN to extract a feature of an image in depth underlie our research. The research aims to classify the two statuses of cancer on gland images using CNN. The training process for six, eight and ten layers exploited on this research. The accuracy obtained up to 82.98, 81.91 and 89.36 percent for six, eight and ten layers respectively. But in the future, we need to improve the computing time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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