Effect of a small number of training cases on the performance of massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT

2003 ◽  
Author(s):  
Kenji Suzuki ◽  
Samuel G. Armato III ◽  
Feng Li ◽  
Shusuke Sone ◽  
Kunio Doi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Dose ◽  
False Positives ◽  
Lung Nodules ◽  
Low Dose Ct ◽  
Artificial Neural

scholarly journals Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network

2017 ◽  
Vol 36 (12) ◽  
pp. 2479-2486 ◽  
Author(s):  
Dufan Wu ◽  
Kyungsang Kim ◽  
Georges El Fakhri ◽  
Quanzheng Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Dose ◽  
Ct Reconstruction ◽  
Low Dose Ct ◽  
Artificial Neural

scholarly journals Development of algorithm for identification of maligant growth in cancer using artificial neural network

2020 ◽  
Vol 10 (6) ◽  
pp. 5709
Author(s):  
R. Pandian ◽  
D.N.S. Ravi Kumar ◽  
R. Raja Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Dose ◽  
Confusion Matrix ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Probability Of Detection ◽  
Low Dose Ct ◽  
Artificial Neural ◽  
Automated Tool

The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The numerous algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the irregular growth of lung tissues. Higher probability of detection is taken as a goal to get an automated tool, with great accuracy. The finest feature sets derived from Haralick Gray level co occurrence Matrix and used as the dimension reduction way for feeding neural network. In this work, a binary Binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix and found in terms of classification accuracy.

Using Artificial Neural Network to Filter Spam for Chinese Mail

2011 ◽  
Vol 55-57 ◽  
pp. 762-766
Author(s):  
Shih Ming Pi ◽  
Hsiu Li Liao ◽  
Su Houn Liu ◽  
Ding Kang Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Text Classification ◽  
False Positives ◽  
The Internet ◽  
Network Approach ◽  
Good Tool ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Computing Speed

As the Internet developed, the problem of spam has become increasingly serious. Not only caused great distress to individuals, but also have a great business costs. With improvements in computing speed, neural network is becoming a very good tool for text classification. The purpose of this study is to conduct few experiments by using neural network approach for Chinese mails’ content. The result shows that neural network approach is effective for Chinese mails’ spam-identification and the adjustments of some parameters (the number of keywords, the number of nodes, and the number of categories) also increase the accurate rate, while reducing false positives.

Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features

2019 ◽  
Vol 10 (10) ◽  
pp. 4135-4149 ◽  
Author(s):  
Furqan Shaukat ◽  
Gulistan Raja ◽  
Rehan Ashraf ◽  
Shehzad Khalid ◽  
Mudassar Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Ct Images ◽  
Lung Nodules ◽  
Artificial Neural
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