  Invasive Coronary Angiography (ICA) images are considered the gold standard
for assessing the state of the coronary arteries. Deep learning classification
methods are widely used and well-developed in different areas where medical
imaging evaluation has an essential impact due to the development of
computer-aided diagnosis systems that can support physicians in their clinical
procedures. In this paper, a new performance analysis of deep learning methods
for binary ICA classification with different lesion degrees is reported. To
reach this goal, an annotated dataset of ICA images that contains the ground
truth, the location of lesions and seven possible severity degrees ranging
between 0% and 100% was employed. The ICA images were divided into 'lesion' or
'non-lesion' patches. We aim to study how binary classification performance is
affected by the different lesion degrees considered in the positive class.
Therefore, five known convolutional neural network architectures were trained
with different input images where different lesion degree ranges were gradually
incorporated until considering the seven lesion degrees. Besides, four types of
experiments with and without data augmentation were designed, whose F-measure
and Area Under Curve (AUC) were computed. Reported results achieved an
F-measure and AUC of 92.7% and 98.1%, respectively. However, lesion
classification is highly affected by the degree of the lesion intended to
classify, with 15% less accuracy when <99% lesion patches are present.
