# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 1 Introduction
Its global dependencies, semantic information modeling, and end-to-end advantages in processing images have made it widely used in computer vision and changed the design paradigm of traditional computer vision models to some extent. Since then, the improved Vit-based methods and their variants have performed well in major vision tasks and have been widely used. However, although the Transformer model has shown much potential in the field of medical images, it also has some drawbacks. Compared to CNN, it has some disadvantages in extracting localized information. This makes the Transformer model not as effective on some medical image datasets where the lesion region accounts for a relatively small amount. In addition, the Transformer model has a more complex structure and more parameters than the traditional CNN model. This is due to the secondary computation and memory overhead associated with the token length. This also results in the training and inference process requiring more time and computational resources, which may pose a significant challenge for resource-limited clinical applications. Meanwhile, multi-layer perceptron (MLP) was revived by the proposal of the MLP-mixer [8], the first pure MLP network architecture that uses MLP instead of convolutional operations in traditional CNN and self-attention in Transformer and achieves very competitive performance in image classification tasks. The emergence of MLP-mixer provoked a new research boom, and many works in the same line of work ensued. The fact that RepMLP [9], CycleMLP [10], HireMLP [11], and other works has been proposed one after another has brought the old structure of MLP back to the stage. However, the excellent performance of MLP usually requires a large amount of training data. For medical image classification tasks with high complexity and a limited number of data, pure MLP-based models may suffer from underfitting, which may affect the performance of the model. Meanwhile, most of the current MLP-based network architectures have the problem of relatively large number of parameters and computations. These computationally expensive models are not lightweight and efficient enough and thus present great challenges in practical clinical applications. Based on the above discussions and observations, we propose a lightweight and efficient medical image classification network (Eff-PCNet) based on pure CNN. Eff-PCNet retains the baseline [12] CNN stage; on the one hand, we propose a multi-branch multi-scale CNN (M2C) module, which utilizes multi-branch and multi-scale operation as an effective alternative to the large kernel convolution. It reduces the computational cost of the module while fusing the feature information between different channels, thus obtaining richer feature information. On the other hand, we introduce the structural reparameterization technique and propose the structural reparameterized CNN (Rep-C) module, which provides a more effective solution for feature reuse. We have conducted extensive experiments using Eff-PCNet on three publicly available medical image classification datasets, and the experimental results show that the classification performance of Eff-PCNet outperforms the currently available methods. As shown in Figures 1 and 2, our Eff-PCNet achieves a better trade-off between the number of parameters and accuracy, and the amount of computation and degree of accuracy on the three publicly available datasets, respectively. In summary, the main contributions of this paper are as follows:  
1. We propose a lightweight and efficient medical image classification network (Eff-PCNet) based on pure CNN, and the experimental results show that it achieves a better trade-off between the number of parameters, the computation, and the classification performance on three publicly available medical image classification datasets. 2. We propose a multi-branch multi-scale CNN (M2C) module, which effectively replaces large kernel convolution with multi-branch multi-scale operation.