tumours This is nothing but identifying correct TP TP
tumours. This is nothing but identifying correct TP–TP pairs.
794
760
are paired with every other lesion from MLO view and
The 155 TP–TP pairs from CC to MLO are now divided into 4 sets
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subsequently the correspondence score of the pair is calculat-
of 31 pairs for training and set of 31 for testing using five-fold
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ed. The lesions on the MLO view which are lying outside the
cross validation using SVM classifier. Similarly, 163 TP–TP
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annular regions defined by the radial distances of all the
pairs from MLO to CC are divided into 4 sets of 32 pairs for
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lesions on CC view are discarded. Thus, there are limited
training and set of 35 for testing using five-fold cross validation
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number of pairs from which one with highest correspondence
using SVM classifier. The correspondence score of the lesion
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score is selected. This highest score actually belongs to the
on CC view extended by combined features set forms a new
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most likely abnormal lesion from suspicious lesions on CC
feature vector. The same experiment for lesions on MLO view
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768
view. In the second experiment, all suspicious lesions on MLO
is performed. The performances of a SVM classifier to
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view are selected one by one and are paired with every other
distinguish the suspicious lesions on CC and MLO view are
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770
lesion from annular region on CC view. Subsequently, the
depicted in Table 6.
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correspondence score of the pair is calculated. All the possible
The average case-based lesions diagnosis sensitivity is
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pairs of lesions from CC to MLO view are divided into 4 sets of
75.91% (from 110 cases) at the cost of 0.69 FPs/I on an average.
807
773
104 pairs for training and remaining set of 104 pairs for five-
The sensitivity here is calculated based on the number of TP–
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fold cross validation using SVM classifier. SVM classified 155 of
TP pairs classified correctly either as benign mass or malignant
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all the pairs as TP–TP and 365 as TP–FP. All the possible pairs of
tumour from actual 110 pairs. The specificity in this KPT330 case is not
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776
lesions from MLO to CC view are divided into 4 sets of 112 pairs
meaningful as we have not selected true negatives (TN) for
811
Please cite this article in press as: Sapate S, et al. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms.
Table 7 – Performance of SVM classifier using fusion scheme on DDSM dataset.
True malignant
False malignant
True benign
False benign
Total pairs
Sensitivity
FPs/I
Kappa
Fig. 8 – Performance of SVM classifier using (a) ROC plot and (b) FROC plot.
812
classification and hence not considered. Similarly, the results
trials are statistically significant. In our experiment, the p
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on DDSM dataset are depicted in Table 7.
value is segments found less than 0.02. Hence the proposed fusion-based
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The average case-based lesions diagnosis sensitivity is
scheme is superior to the single view approach.
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73.65% (from 110 cases) at the cost of 0.72 FPs/I on an average.
Table 8 compares the results of classifier in terms of FPs/I
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Sometimes the classifier performance metrics such as sensi-
for single view, two view and fusion-based scheme at three
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817
tivity, specificity and accuracy are not enough. Especially, the
different adjusted sensitivity levels.
847
818 classifiers built and evaluated on multiple imbalanced data
819 sets can be compared more reliably through the Kappa 5.5. Computational complexity 848
820 statistics. It gives a better indicator of how the classifier
821
performed across all instances of different class distributions.
The preprocessing using fuzzy c-means algorithms has the
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In our experiment as shown in Tables 6 and 7, the values can
complexity of O ¼ ði d n c2Þ where i is number of iterations, d is
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be interpreted as indicators of correlation between observed
number of dimensions (d = 2 in our case), n is number of pixels
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and expected accuracy.
in the given image and c is number of clusters. The
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Fig. 8(a) depicts the performance of the SVM classifier on
computational complexity of segmentation algorithm is O =
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single, two view and fusion image-based CAD scheme using
(n). The computational complexity of feature extraction with
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ROC curves for the classifying malignant tumours from benign
GLCM of a image matrix size M M is O = (M2). The complexity
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masses. Fig. 8(b) shows the case-based performance of SVM