A New Pneumonia Detection Model Based on Transformer with Improved Self-Attention Mechanism
Lecture Notes on Data Engineering and Communications Technologies
; 156:505-514, 2023.
Article
in English
| Scopus | ID: covidwho-2298717
ABSTRACT
Clinical diagnosis based on computed tomography (CT) could be used, as part of diagnosis standard of COVID-19 pneumonia. Addressing the problem that accuracy of CT-based traditional pneumonia classification diagnosis models is relatively low when employed for classification of community-acquired pneumonia (CP), COVID-19 pneumonia (NCP) and normal cases, a new network model is proposed which combines application of Swin Transformer and multi-head axial self-attention (MASA) mechanism, to analyze CT images and make intelligence-assisted diagnosis. The method in detail is to partially replace traditional multi-head self-attention (MSA) mechanism in encoders of Swin Transformer by MASA. The improved model is applied to train and test on commonly used pneumonia CT dataset CC-CCII. The results show that the proposed network outperforms traditional networks ResNet50 and Vision Transformer in indicators of accuracy, sensitivity and F1-measure. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Attention mechanism; Multi-head axial self-attention mechanism; Pneumonia assisted diagnosis; Swin transformer; Computer aided diagnosis; COVID-19; Statistical tests; Attention mechanisms; Clinical diagnosis; Community-acquired pneumonia; Detection models; Diagnosis model; Model-based OPC; Pneumonia assisted diagnose; Computerized tomography
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Lecture Notes on Data Engineering and Communications Technologies
Year:
2023
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS