Efficacy of the echo pattern classification of ovarian tumors 2000 in conjunction with transvaginal ultrasonography for diagnosis of ovarian masses

2015 ◽  
Vol 43 (2) ◽  
pp. 249-255 ◽  
Author(s):  
Atsushi Tajima ◽  
Chikako Suzuki ◽  
Iwaho Kikuchi ◽  
Hanako Kasahara ◽  
Akari Koizumi ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Ovarian Tumors ◽  
Transvaginal Ultrasonography ◽  
Ovarian Masses

In Echo Pattern Classification of Ovarian Masses of JSUM　 2000, the Probability　of Malignant and Borderline Malignant Tumors is Shown as Percentage

2011 ◽  
Vol 37 (8) ◽  
pp. S119-S120
Author(s):  
N. Akamatsu ◽  
S. Fukumoto ◽  
K. Odaka ◽  
Y. Mizutani ◽  
H. Tateiwa ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Malignant Tumors ◽  
Ovarian Masses

scholarly journals OP16.03: Reproducibility of different patterns of benign ovarian masses assessed by transvaginal ultrasonography evaluated by several operators with different degree of experience

2007 ◽  
Vol 30 (4) ◽  
pp. 509-510
Author(s):  
S. Guerriero ◽  
J. L. Alcazar ◽  
M. A. Pascual ◽  
S. Ajossa ◽  
S. Floris ◽  
...  
Keyword(s):  
Transvaginal Ultrasonography ◽  
Ovarian Masses

Classification method of marine target motion pattern based on spatial-temporal trajectories

2021 ◽  
pp. 1-15
Author(s):  
Baichen Jiang ◽  
Wei Zhou ◽  
Jian Guan ◽  
Jialong Jin
Keyword(s):  
Pattern Classification ◽  
Training Sample ◽  
Classification Algorithm ◽  
Classification Model ◽  
Motion Pattern ◽  
Motion Patterns ◽  
Lp Norm ◽  
Sparse Representation Classification ◽  
Route Safety

Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

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