# tumours This is nothing but identifying correct TP TP

tumours. This is nothing but identifying correct TP–TP pairs.
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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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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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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.
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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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lesions from MLO to CC view are divided into 4 sets of 112 pairs
meaningful as we have not selected true negatives (TN) for
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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.

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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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tivity, specificity and accuracy are not enough. Especially, the
different adjusted sensitivity levels.
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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

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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