# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 1 Introduction  
With the rapid development of social productivity and the rapid progress of society, people's living standards are gradually rising, but the number of illnesses and types of diseases are increasing, and a large number of medical images (including X-ray images, CT images, pathology image, and magnetic resonance images) provide a rich reference value for medical diagnosis by imaging physicians. Traditionally, radiologists and clinicians have performed the majority of medical image interpretation. However, their analysis is vulnerable to inter-observer variability, fatigue, and significant omission rates. As a result, clinicians are unable to consistently analyze and interpret medical data withhigh accuracy. However, with the development of deep learning, the use of machine learning algorithms in medicine is becoming more common. By analyzing the features of input images, abnormalities can be detected faster by machine learning algorithms in medical imaging. Computer-aided diagnosis and treatment can effectively solve the above problems, make up for the shortcomings of manual diagnosis, and relieve the work pressure experienced by doctors. However, medical images are usually characterized by a relatively limited size and a low signal-to-noise ratio. It is difficult for deep learning algorithms to locate abnormalities based on image content alone. Therefore, knowing how to correctly and quickly classify and segment medical images to help doctors identify patient foci and conduct disease diagnosis is a thorny problem in the field of medical image analysis. In recent years, with the rapid development of high-performance computing resources, deep learning techniques have made a big splash in the field of computer vision. Since AlexNet [1] started to make its mark in the ImageNet [2] image classification competition in 2012, it set records for accuracy and low error, beating all other models by rate. Convolutional neural networks (CNN) gained the attention of all research scholars in the field. In the following decade, many scholars explored and researched deep learning theory, and various high-performance CNN models were proposed one after another. CNN became indispensable in major vision tasks and achieved a lot of success. Influenced by this, some researchers have also tried to apply CNN to medical image analysis tasks [3; 4; 5]. Since the convolutional operation establishes a close connection between a pixel in an image and the surrounding pixel points, this property allows CNN-based models to better extract information about local details in an image. Therefore, these methods also show good classification performance in medical image analysis tasks. However, these methods suffer from the problem of large number of parameters and computational effort. Meanwhile, since the convolutional operation itself can only extract local features, it has certain deficiencies in remote modeling capability. In response to these problems with CNN, many researchers have tried to find new solutions. As the Transformer model [6] was proposed in the field of natural language processing (NLP) and achieved significant results in various NLP tasks, researchers have tried to apply it in the field of computer vision. Vit [7] is a pioneering work on vision Transformers that splits an image into small patches, each of which is considered as a wordor token. The success of Vit validates the feasibility of a pure Transformer architecture for computer vision tasks. The self-attention mechanism in Transformer is a key component that helps the model to capture global dependencies when processing sequential data. In computer vision, this mechanism can help the model focus on the associations between different locations when processing an image, to better understand the global structure and contextual information of the image. The development of the Transformer model in computer vision has brought new ideas and performance breakthroughs for tasks such as image classification, target detection, and image generation.