# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 3 Methodology
### Overall Architecture  
In recent years, network architectures such as Transformer- and MLP-based have made a big splash in the field of computer vision, and Transformer-based methods seem to have replaced CNN-based methods for many tasks. However, in medical image classification tasks, Transformer- and MLP-based methods require a large number of training data to show their effective performance, and these models are large and complex, which makes them difficult to advance in practical clinical applications. Therefore, we believe that designing an efficient and lightweight CNN model is a must. In this paper, we propose Eff-PCNet, an efficient medical image classification network based on pure CNN. It achieves better classification performance than Transformer- and MLP-based methodswith fewer parameters and less computation and is able to classify dermatological and preumonic diseases quickly and accurately. Eff-PCNet is a lightweight efficient medical image classification network, and its overall network architecture is shown in the upper side of Figure 3. It mainly consists of a stem layer and 8 CNN stages, and we keep the structure of stages 1, 2, and 3 and stages 5, 7, and 8 in the CNN stages similar to the baseline, and the number of repetitions of each stage consistent with the baseline. Stage 4 and stage 6 are our proposed M2C module and Rep-C module (because the position of our proposed module is set in the whole network architecture; we will explain the reason for this in the ablation experiment in Section 4.5). When the input image \(X_{in}\in\mathrm{R}^{3\times H\times W}\) enters the network, it first enters the stem layer, which consists of a 3 \(\times\) 3 convolution with a stride of 2, a BN [37] layer, and a Swish [38] activation function, and the output feature map after downsampling in the stem layer is \(F_{1}\in\mathrm{R}^{24\times\frac{H}{2}\times\frac{W}{2}}\). Then, stages 1, 2, and 3 are repeated 2, 4, and 4 times, respectively, on the fused mobile inverted bottleneck (FMB) module, and the output feature map is \(F_{2}\in\mathrm{R}^{64\times\frac{H}{2}\times\frac{W}{8}}\). The structure of the FMB module is shown in the lower side of Figure 3, which mainly contains a CBA block consisting of the Conv, BN, and Act layers, and a CB block consisting of the Conv and BN layers. Its working principle can be summarized as follows:  
\[ \begin{array}{l}X_{1}=Act(BN(Conv(X))),\\ X_{2}=BN(Conv(X)),\end{array}  \tag{1}\]  
where \(Conv\) is denoted as regular convolution, \(BN\) is BatchNorm, and \(Act\) is the activation function. Stage 4 is our proposed M2C module. We divide the feature map \(F_{2}\in\mathrm{R}^{64\times\frac{H}{2}\times\frac{W}{8}}\) after entering M2C into four parallel branches along the channel dimension by a certain ratio  
Figure 3: Eff-PCNet overall network architecture. factor. One of the branches is a identity mapping, and the remaining three branches utilize deep convolution at different scales to complete feature extraction; these concatenate along the channel dimensions to fuse the multi-scale and multi-dimensional feature information. The input feature map enters the M2C module after the number of channels, width and height do not change, and the final output feature map is \(F_{3}\in\mathbb{R}^{64\times\frac{W}{8}\times\frac{W}{8}}\). Stage 5 is the mobile inverted bottleneck convolution (MBC) module, whose structure is shown in the lower side of Figure 3, and it mainly consists of two CBA blocks: the SE block and the CB block. Its working principle can be summarized as follows:  
\[ \begin{array}{l}X_{1}=\mathit{Act}(BN(Conv(X))),\\ X_{2}=\mathit{Act}(BN(Conv(X_{1}))),\\ X_{3}=\mathit{SE}(X_{2}),\\ X_{4}=\mathit{BN}(Conv(X_{3})),\end{array}  \tag{2}\]  
where \(Conv,\mathit{BN}\), and \(Act\) are the same as in Equation (1). \(\mathit{SE}\) denotes convolution, and SiLU [39] denotes activation function, convolution, and Sigmoid [40] activation function in that order.