计算机工程与应用2024，Vol.60Issue(20)：244-253,10.DOI:10.3778/j.issn.1002-8331.2306-0386
SFA-ConvNeXt:逐级聚合多尺度ConvNeXt的皮肤镜图像分类
SFA-ConvNeXt:Dermoscopic Image Classification for Stepwise Aggregation of Multiscale ConvNeXt
摘要
Abstract
Early detection of skin cancer significantly improves the five-year survival rate for patients.However,due to the subtle nature of early malignant tumors in the skin,their symptoms are not apparent,and specialized doctors need to perform multiple biopsies and extract lesion tissues to diagnose the type of lesion.Traditional artificial intelligence methods have low accuracy in identifying skin lesion images primarily because they are difficult to simultaneously focus on spatial details and shallow semantic features.To effectively represent spatial positions and shallow feature information,and avoid the model being overly concerned with detailed information,which can lead to misclassification of easily distinguishable images,a progressive aggregation attention network based on ConvNeXt is proposed.This method utilizes a hierarchical ConvNeXt encoder to extract deep and shallow features of lesion regions layer by layer.By employing parallel spatial attention,it effectively integrates spatial position information with deep or shallow semantic features,aggregating multi-scale contextual information.Meanwhile,a progressive feature aggregation module is designed to effectively integrate deep and shallow features and aggregate them by dynamically adjusting weights,closely aligning with the process of rough observation and meticulous examination in skin image classification by specialized doctors.Experimental tests on the ISIC2018 and ISIC2019 datasets show that the accuracy,precision,recall,and F1-Score of this method are 95.27%,93.76%,92.83%,and 93.18%,respectively,for ISIC2018,and 92.63%,91.06%,87.05%,and 88.81%,respectively,for ISIC2019.Compared to ConvNeXt,the accuracy is improved by 2.13 and 3.29 percentage points respectively,demon-strating its ability to effectively extract detailed and rough features,providing new evidence for the diagnosis of skin image through dermatoscopy.关键词
ConvNeXt/并行空间注意力/逐级特征聚合/皮肤镜图像/图像分类Key words
ConvNeXt/parallel spatial attention/stepwise feature aggregation/dermatoscopic images/image classification分类
计算机与自动化引用本文复制引用
王泽彤,张俊华,王肖..SFA-ConvNeXt:逐级聚合多尺度ConvNeXt的皮肤镜图像分类[J].计算机工程与应用,2024,60(20):244-253,10.基金项目
国家自然科学基金(62063034). （62063034）
