Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.
Med Biol Eng Comput
; 62(5): 1589-1600, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38319503
ABSTRACT
This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
3_ND
Base de dados:
MEDLINE
Assunto principal:
Médicos
/
Tuberculose Pulmonar
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Med Biol Eng Comput
Ano de publicação:
2024
Tipo de documento:
Article