# MSMP-Net: A Multi-Scale Neural Network for End-to-End Monkeypox Virus Skin Lesion Classification
## 1 Introduction  
Monkeypox is a viral disease caused by monkeypox virus infection. Since the first human case of monkeypox virus was reported in the UK on 7 May 2022, cases of monkeypox virus infection have occurred worldwide [1]. On 23 June 2022, the World Health Organization announced that monkeypox poses a \"moderate risk\" to global public health. Monkeypox is a zoonotic infectious disease caused by monkeypox virus. Its clinical symptoms are similar to smallpox, such as high fever, fatigue, headache, rash, etc. Monkeypox is easily transmitted among people, posing a major threat to human health. In addition, monkeypox infection is similar to other poxvirus infections and is difficult to diagnose based on clinical symptoms alone [2], as shown in Figure 1. Currently, a variety of nucleic acid detection methods have been studied for the detection of monkeypox virus, such as the fluorescent PCR method [3]. This method needs to be carried out in a PCR laboratory and strictly follow the principle of zoning operation. Rapid detection of monkeypox virus is crucial for the timely diagnosis of monkeypox cases and the control of monkeypox epidemics. It is necessary to establish a rapid detection method for monkeypox virus. In recent years, with the continuous development of deep learning technology, a series of breakthroughs have been made in the fields of image classification [4], object detection [5], and semantic segmentation [6]. At the same time, computer-aided diagnosis technology, as an emerging technology, uses deep learning methods to analyze medical images or patient non-image data, which can not only evaluate the patient's condition but also help clinicians make decisions. [PERSON] et al. [7] constructed a monkeypox skin disease dataset and compared the classification effect of monkeypox skin disease through different deep network models. [PERSON] et al. [8] compared 13 different pre-trained deep learning models for monkeypox virus detection and finally used an integrated model method toobtain the best diagnostic effect. [PERSON] et al. [9] used transfer learning methods to construct a mobile monkeypox skin disease diagnosis method. [PERSON] et al. [10] diagnosed monkeypox skin disease based on a residual network model by fusing attention and deep separable convolution modules. [PERSON] et al. [11] used transfer learning technology to construct a lightweight BinaryDNet53 for monkeypox virus detection. This algorithm is suitable for mobile devices with limited resources. Compared with references [7; 8; 9; 10], reference [11] has six categories, and its accuracy is not high. Monkeypox image classification also meets the following problems: (1) the size, shape, color, and distribution of monkeypox lesions in different patients may vary greatly; (2) there is a scarcity of annotated medical images for monkeypox, which hampers the training of robust deep learning models; and (3) as previously addressed, the dataset contains an unequal number of images across different classes, leading to potential biases in model training. In summary, this paper introduces MSMP-Net, a novel monkeypox virus classification method based on ConvNeXt and multi-scale feature fusion, designed for rapid detection of monkeypox virus skin diseases. The main contributions of this study are as follows:  
1. [label=(0)]
2. Enhanced Feature Extraction with ConvNeXt: By utilizing ConvNeXt [12] as the backbone network, we not only improve the network's feature extraction capabilities, but also reduce computational complexity. 3. Proposed Multi-Scale Feature Fusion Structure: We introduce a multi-scale feature fusion architecture to achieve complementary fusion of features at different scales. This enhances the feature representation of monkeypox virus images and improves the diagnostic performance for monkeypox virus detection. 4. End-to-End Classification: MSMP-Net is designed for end-to-end classification, allowing seamless integration of feature extraction and classification processes.