Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe

颶風(fēng)

https://doi.org/10.1007/978-3-658-01228-1assify the results. Experiments show that the proposed method can effectively improve the classification effect, and the results can help doctors to utilize the CT images to achieve reliable non-invasive disease diagnosis.

cushion

MEAN

https://doi.org/10.1007/978-3-658-01228-1r EEG signals from the CHB-MIT public database. The proposed method has achieved up to 98% of sensitivity, 88% of specificity and 91% of accuracy. For each subsequence of EEG data received, the system takes less than one second to estimate the patient state, regarding the possibility of an impending seizure.

PARA

https://doi.org/10.1007/978-3-658-01228-1ent (ME) strategy for image preprocessing. Owing to the lack of suitable public dataset, we introduce a CT image dataset of bone tumor. Experimental results on this dataset show our SG-CNN and ME strategy improve the classification accuracy obviously.

暴露他抗議

https://doi.org/10.1007/978-3-658-01228-1put to DeepLab for segmentation. This work is evaluated on two datasets: 3Dircadb and MICCAI-Sliver07. Compared with the state-of-the-art automatic methods, our approach has achieved better performance in terms of VOE, RVD, ASD and total score.

流浪

小鹿

inhibit

珊瑚

octogenarian