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4.3.2 New Method
Kondo, Hiroshi
(5)
The outputs of NN correspond to 0.6 — 3.0mm ¢ rounded opacities by step 0.2mm. As for the opacities bigger than
Output Error
0.3 d
0.2
0.1 +
0.0 |
| | 1 te [771 {
0 1710° .2°10* 3" 10°
Fig.9 Output error-repeating times
Characteristic curve
più p12 5-14 ali
NY
0-0 r- r— +4 r-36 rn r—40
. x Te « gn «
4
3mm¢, the ROI image is reduced into 1/5 scale by
averaging. Hence the size of each output corresponds to
3.0 — 15.0mm 4 . It means that the same NN can be
utilized for detection of 3.0 ~ 15.0mm 4 . It is sufficient
to detect a rounded opacities with the size until 15
mm because the rounded opacities bigger than 15.0
mm ¢ is very rare for the pneumoconiosis X-ray photos.
In this paper the most popular opacities in
pneumoconiosis Le., small rounded opacities are only
detected.
The convergence of the back propagation NN is made
after 17321 repeating times. The repeating is basically
done according to a maximum principal. Figure 9 shows
the output error-repeating times Characteristic curve.
This is one of
the typical curve of the back propagation method. The
input image to the NN is not of bi-level because of the
averaging for the size bigger than 3mm¢ . Since the
figure of the rounded opacities is quite different with a
circle for making a bi-level image multi-level image is
much better as a neural network input for the bigger size
3mm¢ ~ 15mm¢.
5. TRAINIG PATTERN
Back propagation NN requires training patterns. Figure
10 shows several training patterns for pneumoconiosis
rounded opacities with several sizes. The last five
elliptic figures are for unnecessary parts like vessel and
rib shade. These are actually not rounded opacities but
thin and long ones. Hence such training patterns are
effective for reduction the false positive. The big
rounded opacities are made by averaging 5 ~ 5 pixel and
put it into one pixel.
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B5. Amsterdam 2000. 457