NDLI: Effect of Training Artificial Neural Networks on 2D Image: An Example Study on Mammography

Content Provider	IEEE Xplore Digital Library
Author	Xuejun Zhang Fujita, H. Jing Chen Zuojun Zhang
Copyright Year	2009
Abstract	Several structures of artificial neural networks (ANNs) with different training patterns were investigated so as to compare their performances on detecting the cluster of microcalcifications (CM) on mammography. 150 region-of-interests (ROIs) around mass containing both positive and negative microcalcifications were selected for training the network by a standard or modified error-back-propagation algorithm. A rule-based triple-ring filter (TRF) was used for evaluating the performances of these two different types of methods. The results showed that the shift-invariant artificial neural network (SIANN) was the best ANN model to detect CM, while SIANN and TRF had different ability of detecting microcalcifications. In a practical detection of 30 cases with 40 clusters in masses, the sensitivity of detecting CMs was improved from 90% by our previous method to 95% by using both SIANN and TRF.
Starting Page	214
Ending Page	218
File Size	1365408
Page Count	5
File Format	PDF
ISBN	9781424438358
DOI	10.1109/AICI.2009.475
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Microcalcification Neurons Artificial neural networks Collision mitigation Mammogram Mammography Electronic mail Mass Filters Cities and towns Computer-aided diagnosis (CAD) Artificial neural network Artificial intelligence Triple-ring filter analysis Medical diagnostic imaging Biomedical imaging
Content Type	Text
Resource Type	Article

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