2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY)最新文献_第2页

The traffic condition study of the roadside for the takeaway service robot 外卖服务机器人的路边交通状况研究

2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2020-11-04 DOI: 10.1109/iFUZZY50310.2020.9297364

Bo-Shen Chen, Kuo-Yi Chen

引用次数: 0

Activity Recognition Based on DCNN and Kinect RGB Images 基于DCNN和Kinect RGB图像的活动识别

2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2020-11-04 DOI: 10.1109/iFUZZY50310.2020.9297815

Tan-Hsu Tan, Munkhjargal Gochoo, Honglin Chen, Shing-Hong Liu, Yung-Fa Huang

引用次数: 1

Combining VGG16, Mask R-CNN and Inception V3 to identify the benign and malignant of breast microcalcification clusters 结合VGG16、Mask R-CNN、Inception V3鉴别乳腺微钙化簇的良恶性

2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2020-11-04 DOI: 10.1109/iFUZZY50310.2020.9297809

Ya-Chu Hsieh, Chiun-Li Chin, Chia-Shin Wei, I-Miao Chen, Pin-Yu Yeh, Ru-Jiun Tseng

{"title":"Combining VGG16, Mask R-CNN and Inception V3 to identify the benign and malignant of breast microcalcification clusters","authors":"Ya-Chu Hsieh, Chiun-Li Chin, Chia-Shin Wei, I-Miao Chen, Pin-Yu Yeh, Ru-Jiun Tseng","doi":"10.1109/iFUZZY50310.2020.9297809","DOIUrl":"https://doi.org/10.1109/iFUZZY50310.2020.9297809","url":null,"abstract":"Breast cancer has the highest incidence among women in Taiwan, which is mainly screened by mammography. When the doctor observes the mammogram and initially judges that the patient has malignant microcalcification (MC) clusters, the patient must undergo needle localization surgical biopsy. However, needle localization surgical biopsy makes the patient painful, and the color of breast tissue and MCs are all white, which makes it difficult for doctors to judge where MCs are clustered immediately. Thus, we use VGG16 to find out breast MC clusters from the image. Moreover, we use Mask RCNN to find MCs from the clusters to remove the noise from the background. Finally, we use Inception V3 to identify the benign and malignant of MC clusters. The accuracy of the cluster classification, MCs labeling and benign and malignant analysis are 93%, 95% and 91%. Furthermore, the precision, specificity and sensitivity of our proposed methods are about 87%, 89% and 90%, respectively. It proved that our system can effectively assist doctors in diagnosing and reduce the burden on patients and medical personnel.","PeriodicalId":215352,"journal":{"name":"2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126706319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface 类失衡处理的自适应子空间采样——一些澄清、算法及在脑机接口中的应用

2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2019-05-26 DOI: 10.1109/iFUZZY50310.2020.9297814

Chin-Teng Lin, Kuan-Chih Huang, N. Pal, Zehong Cao, Yu-Ting Liu, Chieh-Ning Fang, Tsung-Yu Hsieh, Yang-Yin Lin, Shang-Lin Wu

{"title":"Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface","authors":"Chin-Teng Lin, Kuan-Chih Huang, N. Pal, Zehong Cao, Yu-Ting Liu, Chieh-Ning Fang, Tsung-Yu Hsieh, Yang-Yin Lin, Shang-Lin Wu","doi":"10.1109/iFUZZY50310.2020.9297814","DOIUrl":"https://doi.org/10.1109/iFUZZY50310.2020.9297814","url":null,"abstract":"Kohonen’s Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen’s ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-the-art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem.","PeriodicalId":215352,"journal":{"name":"2020 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132917414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0