# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 3 Materials and Methods
### The Inception-v2 Network  
The Inception-v2 [37] network architecture is designed to enhance the network performance through a number of key techniques. These include the decomposition of the 7 \(\times\) 7 convolution, the utilisation of the RMSProp optimisation algorithm, the implementation of a label smoothing strategy, and the incorporation of an Inception module [38]. 1. Convolutional decomposition: Inception-v2 decomposes the 7 \(\times\) 7 convolution into two one-dimensional convolutions (1 \(\times\) 7 and 7 \(\times\) 1) and the 3 \(\times\) 3 convolution into two one-dimensional convolutions (1 \(\times\) 3 and 3 \(\times\) 1). This decomposition has two beneficial effects: it speeds up the computation and increases the nonlinearity of the network, resulting in an increase in both the depth and width of the network. 2. An update to the optimisation algorithm was implemented in Inception-v2, which now utilises the RMSProp optimisation algorithm in place of the previous SGD algorithm. The RMSProp algorithm was selected due to its superior ability to adapt to the requirements of network training through the introduction of an accumulation term, which enables the historical gradient to contribute to the alteration of the learning rate with designated weighting. 3. In order to mitigate the adverse consequences of labelling errors, Inception v2 employs label smoothing. This approach entails modifying the loss function of the samples so that the output of the label is no longer exclusively 1 or 0 but rather 0.8 or 0.88 approaching 1 and 0.1 or 0.11 approaching 0. This results in the realisation of label smoothing. For each Inception module, the inputs are replicated four times for distinct layers. This comprises three 1 \(\times\) 1 + 1(S) convolutional layers and one 3 \(\times\) 3 + 1(S) maximum pooling layer. The three convolutional layers in the second layer are 1 \(\times\) 1 + 1(S), 3 \(\times\) 3 + 1(S), and 5 \(\times\) 5 + 1(S), which are capable of acquiring feature patterns at different scales. All the convolutional layers in this structure utilise the ReLU activation function. Figure 1: General flowchart of the proposed method. In the Inception module, all the layers have a step size of 1 and utilise the same padding, ensuring that the output of each layer has identical dimensions (the depth can vary). Given that each output has the same dimensions, they can be stacked in the depth direction to form a depth concat layer. This can be achieved by utilising the concat() function in TensorFlow, with axis = 3 (axis 3 is the depth direction). In comparison to the input, the bottleneck layer outputs a reduced number of feature maps, thereby reducing the dimensionality. This is the rationale behind the term 'bottleneck layer'. This approach is particularly beneficial when utilised prior to computationally demanding convolutional layers, such as 3 \(\times\) 3 and 5 \(\times\) 5. The structure of 1 \(\times\) 1, 3 \(\times\) 3 and 1 \(\times\) 1, 5 \(\times\) 5 forms a more efficient convolutional layer capable of acquiring more complex feature patterns. Indeed, this combination of convolutional layers is equivalent to a two-layer neural network rather than simple linear classification when traversing a scan of the input image. For further details, please refer to Figures 2 and 3, which show diagrams of the Inception and Inception-v2 network models. As shown in Figure 2, the Inception module processes input feature maps in parallel through convolutional kernels of three different sizes, 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5, as well as using a 3 \(\times\) 3 max pooling layer, and integrates them at the end to generate richer feature representations. The 1 \(\times\) 1 convolutional kernels play a crucial role in this structure, as they are not only used for feature extraction but also to reduce (or increase) the dimensionality of the feature maps, thereby enhancing the model's expressive power and training efficiency without significantly increasing the computational complexity.