基于Swin Transformer的疟疾细胞图像识别研究

Malaria cell image recognition based on Swin Transformer

为了协助医务人员更准确、更快速地诊断疟疾,提出一种基于Swin Transformer(SwinT)的疟疾细胞图像识别方案。方案采用伪彩色图像增强算法对血片图像进行预处理,以突出图像的颜色对比度,并引入SwinT模型作为主干网络,解决下采样固定和全局信息无法交互的问题,同时引入卷积层对图像进行线性变换,构建残差网络解决梯度消失和梯度爆炸问题。实验表明,与图像量化等其他图像增强方法相比,本文方法增强了疟疾细胞图像的色彩对比度,改进后方案的准确率达到99.7%,高于现有文献方法,可以对疟疾的辅助治疗带来更有价值的支持。

A Swin Transformer(SwinT)-based scheme for malaria cell image recognition is proposed to assist medical personnel in diagnosing malaria more accurately and quickly.The scheme pre-processes blood films with a pseudo-color image enhancement algorithm to highlight the color contrast,and uses SwinT model as the backbone network to solve the problems of fixed downsampling and the inability to interact with global information,while introducing a convolutional layer for linear transformation and constructing a residual network to address the issues of gradient disappearance and gradient explosion.Experiments show that compared with other image enhancement methods such as image quantization,the proposed method enhances the color contrast of malaria cell images.The accuracy of the improved scheme reaches 99.7%,higher than the existing literature methods and bringing more valuable support to the adjunctive treatment of malaria.