Deep Learning Application for Effective Classification of Different Types of Psoriasis

Table 3

Mathematical operations of CNN and LSTM.

CNN

LSTM

CNNs are feed-forward neural networks in which learning is achieved pixel by pixel [37]. CNN employs convolution kernel , a matrix that moves over the input images and executes a dot product with the central region of the input data, represented by . Following this, the output is yielded as matrix of the dot products with columns and rows and is represented by the following expression;

The LSTM deep learning algorithm involves working on a loop network that has two hidden states: cell state and hidden state. Furthermore, it involves assigning weights W as learning parameters for the classification algorithm [38]. X2 denotes the LSTM layers with the hidden layers being responsible for carrying feedback. The following expression exhibits how the algorithm works with sigmoid function and the inclusion of past values;