Abstract

Info

Author(s):: HE Pengfei1; MA Jianfei1; LI Chenglin1; ZHANG Tongjing2; LIANG Dawei3; 1. School of Physics and Electronic Information, Yantai University, Yantai 264005, China 2. Yantai Power Plant, Huaneng Shandong Power Generation Co., Ltd, Yantai 264002, China 3. Yantai Center for Food and Drug Control, Yantai 264000, China

Keywords:: Keywords: medical image classification Swin Transformer pseudo-color image processing residual structure SW-MSA

Abstract:: Abstract: 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.