  In this study, we proposed a model for skin disease classification using a
Bilinear Convolutional Neural Network (BCNN) with a Constrained Triplet Network
(CTN). BCNN can capture rich spatial interactions between features in image
data. This computes the outer product of feature vectors from two different
CNNs by a bilinear pooling. The resulting features encode second-order
statistics, enabling the network to capture more complex relationships between
different channels and spatial locations. The CTN employs the Triplet Loss
Function (TLF) by using a new loss layer that is added at the end of the
architecture called the Constrained Triplet Loss (CTL) layer. This is done to
obtain two significant learning objectives: inter-class categorization and
intra-class concentration with their deep features as often as possible, which
can be effective for skin disease classification. The proposed model is trained
to extract the intra-class features from a deep network and accordingly
increases the distance between these features, improving the model's
performance. The model achieved a mean accuracy of 93.72%.
