宿州学院学报2023，Vol.38Issue(12)：11-17,7.DOI:10.3969/j.issn.1673-2006.2023.12.003
基于SPD多尺度输入的ST-MASA的肺炎智能检测模型
Intelligent Pneumonia Detection Model of ST-MASA Based on SPD Multi-scale-input
摘要
Abstract
The clinical diagnosis results based on lung X-rays provide important evidence in the COVID-19 pneu-monia diagnosis process and for some other pneumonia.However,due to the similarity of the lesions among many types of pneumonia displayed by X-rays,and due to the huge amount of X-ray film reading of a doctor's daily work,there are some problems such as misdiagnosis,missed diagnosis and time consumption in traditional X-ray film rea-ding and identification.Therefore,an intelligent pneumonia detection model of ST-MASA(Swin Transformer with Multi-Head Axial-Self-Attention)that integrates the Spatial pyramid decomposition(SPD)module for multi-scale in-put is proposed for the automatic classification of COVID-19 and multi-type pneumonia.The model will also be able to automatically focus on the discrimination information and multi-scale characteristics of pneumonia lesions,and further better classify COVID-19,Lung_Opacity,non-covid viral pneumonia and Normal X-ray films so as to better help radiologists to carry out medical diagnosis work.The experimental results show that the proposed model is supe-rior to the classical network models ResNet 50,ResNet 101,Inception net-V3 and Swin Transformer in terms of ac-curacy,recall rate and F1-Measure.关键词
肺炎智能检测/空间金字塔分解/多尺度输入/多头轴向自注意力机制/Swin TransformerKey words
Intelligent detection of pneumonia/Spatial pyramid decomposition/Multi-scale input/Multi-head Axial-Self-Attention/Swin Transformer分类
信息技术与安全科学引用本文复制引用
李芳芳,束建华,阚峻岭,殷云霞,孙大勇,马春..基于SPD多尺度输入的ST-MASA的肺炎智能检测模型[J].宿州学院学报,2023,38(12):11-17,7.基金项目
安徽省高等学校自然科学重点研究项目(2022AH050475 （）
KJ2020A0394) （）
安徽中医药大学自然科学重点研究项目(2020zrzd20 （）
2020zrzd19 （）
2020zrzd17 （）
2021zrzd12) （）
安徽省省级教学研究项目(2020jyxm1018 （）
2020jyxm1020). （）
