Confidence Analysis for Breast Mass Image Classification

Computer-aided diagnosis (CAD) has great potential in pro-
viding real benefits to doctors and patients. Recent studies
have, however, found lack of trust in CAD by radiologists in
clinical diagnostic decision making. One of the main reasons
is the lack of an appropriate confidence measure. This pa-
per presents the first-ever study of classification confidence
in the context of breast mass classification. We evaluated 11
state-of-the-art classification algorithms on breast mass image
data using their confidence of classification metric, in addi-
tion to other standard evaluation metrics including accuracy
and area under the curve (ROC). Experimental results show
that although most classifiers produced very similar results
with less than 2% difference in terms of accuracy and ROC,
their performances are significantly different in terms of con-
fidence levels. We suggest that the confidence measure should
be used in conjunction with the existing performance metrics
such as accuracy and ROC.