Prescreening entire mammograms for masses with artificial neural networks: preliminary results.
OBJECTIVE
The authors evaluated the feasibility of combining wavelet transform and artificial neural network (ANN) technologies to prescreen mammograms for masses.


METHODS
Fifty-five mammograms (29 with masses and 26 without) were digitized to 100-mm resolution and processed by using wavelet transformation. These wavelets were subjected to a linear output sequential recursive auto-associative memory ANN and cluster analysis with feature vector formation. These vectors were used in two separate experiments-one with 13 cases and another with seven cases held out in a test set-to train feed-forward ANNs to detect the mammograms with a mass. The experiments were repeated with rerandomization of the data, four and six times, respectively.


RESULTS
There was a statistically significant correlation (P < .01) between the network's prediction of a mass and the presence of a mass. With majority voting, the feed-forward ANNs detected masses with 79% sensitivity and 50% specificity.


CONCLUSIONS
Although preliminary, the combination of wavelet transform and ANN is promising and may provide a viable method to prescreen mammograms for masses with high sensitivity and reasonable specificity.
Kalman
B L
BL
Mallinckrodt Institute of Radiology, Barnes-Jewish Hospital, St Louis, MO 63110, USA.
Reinus
W R
WR
Kwasny
S C
SC
Laine
A
A
Kotner
L
L
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography; D008875:Middle Aged; D016571:Neural Networks, Computer; D010865:Pilot Projects; D011857:Radiographic Image Interpretation, Computer-Assisted