WEKO3
インデックスリンク
アイテム
A Breast Cancer Classifier using a Neuron Model with Dendritic Nonlinearity
http://hdl.handle.net/2297/44377
http://hdl.handle.net/2297/443776f445c28-ab2f-4dbd-adaf-16a5f22416e2
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
TE-PR-TODO-Y-1365.pdf (1.5 MB)
|
|
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-10-03 | |||||
| タイトル | ||||||
| タイトル | A Breast Cancer Classifier using a Neuron Model with Dendritic Nonlinearity | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| 著者 |
Sha, Zijun
× Sha, Zijun× Hu, Liu× Todo, Yuki× Ji, Junkai× Gao, Shangce× Tang, Zheng |
|||||
| 書誌情報 |
IEICE TRANSACTIONS on Information and Systems 巻 E98-D, 号 7, p. 1365-1376, 発行日 2015-07-01 |
|||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1745-1337 | |||||
| NCID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA11510296 | |||||
| DOI | ||||||
| 関連タイプ | isIdenticalTo | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1587/transinf.2014edp7418 | |||||
| 出版者 | ||||||
| 出版者 | IEICE = 電子情報通信学会 / Wiley-Blackwell | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research. Copyright © 2015 The Institute of Electronics, Information and Communication Engineers. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Embargo Period 6 months | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
| 関連URI | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.jstage.jst.go.jp/browse/transinf | |||||
| 関連URI | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.ieice.org/jpn/ | |||||
