Name
Title | ||
|---|---|---|
Detection of suspected malignant patterns in three-dimensional magnetic resonance breast images. |
Abstract | ||
|---|---|---|
In this article, a Boolean Neural Network (BNN) is used for the detection of suspected malignant regions in 3D breast magnetic
resonance (MR) images. The BNN is characterized by fast learning and classification, guaranteed convergence, and simple, integer
weight calculations. The BNN learning algorithm is incremental, which allows the addition and deletion of training patterns
without unclearning those already learned. The incremental learning algorithm automatically reduces the training set and trains
the network only with those examples estimated to be useful. The architecture is suitable for parallel hardware implementation
using available Very Large Scale Integration (VLSI) technology. The BNN was trained by using a set of malignant, benign, and
false-positive patterns, extracted by experts, from selected MR studies, by using an incremental learning algorithm. After
training, the network was tested by means of a consistency checking test, cross validation techniques, and patterns from actual
MR breast images. During the consistency test, the BNN was tested by using the same patterns used for training. The BNN classification
accuracy in this case was 99.75%, proving the ability of the BNN to select useful patterns from the training set. Then, a
leave one out cross-validation (LOOCV) test was done by using patterns from the training set and the classification accuracy
was 90%. Next, an extended training set was created by shifting the original patterns in different directions. A cross-validation
test was then performed by dividing the set of patterns into a training and a test set. Classification accuracy was compared
to the nearest neighbor classifier. Results showed that the BNN achieved an average of 77% classification accuracy while requiring
only 34% of the original training set. On the other hand, the nearest neighbor classifier achieved an accuracy of 57.9% while
retaining the whole training set. Another test using actual MR slices different from the training set was done and results
compared favorably to a radiologist’s findings. Test results show the BNN’s capability to detect suspected malignant regions
in 3D MR images of the breast. The proposed BNN architecture can save the radiologist a great deal of time browsing MR slices
searching for suspected malignancies. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/BF03168730 | J. Digital Imaging |
Keywords | Field | DocType |
breast cancer,magnetic resonance imaging (MRI),neural networks,boolean neural networks | Training set,Convergence (routing),Computer science,Artificial intelligence,Artificial neural network,Cross-validation,Very-large-scale integration,Machine learning,Magnetic resonance imaging,Nearest neighbor classifier,Test set | Journal |
Volume | Issue | ISSN |
11 | 2 | 0897-1889 |
Citations | PageRank | References |
6 | 1.81 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Essam A. El-Kwae | 1 | 111 | 11.06 |
| Joel E. Fishman | 2 | 6 | 1.81 |
| Maria J. Bianchi | 3 | 6 | 1.81 |
| Pradip M. Pattany | 4 | 6 | 1.81 |
| Mansur R. Kabuka | 5 | 250 | 16.95 |