Multimodal medical image fusion using convolutional neural network and extreme learning machine.
Front Neurorobot
; 16: 1050981, 2022.
Article
em En
| MEDLINE
| ID: mdl-36467563
ABSTRACT
The emergence of multimodal medical imaging technology greatly increases the accuracy of clinical diagnosis and etiological analysis. Nevertheless, each medical imaging modal unavoidably has its own limitations, so the fusion of multimodal medical images may become an effective solution. In this paper, a novel fusion method on the multimodal medical images exploiting convolutional neural network (CNN) and extreme learning machine (ELM) is proposed. As a typical representative in deep learning, CNN has been gaining more and more popularity in the field of image processing. However, CNN often suffers from several drawbacks, such as high computational costs and intensive human interventions. To this end, the model of convolutional extreme learning machine (CELM) is constructed by incorporating ELM into the traditional CNN model. CELM serves as an important tool to extract and capture the features of the source images from a variety of different angles. The final fused image can be obtained by integrating the significant features together. Experimental results indicate that, the proposed method is not only helpful to enhance the accuracy of the lesion detection and localization, but also superior to the current state-of-the-art ones in terms of both subjective visual performance and objective criteria.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Front Neurorobot
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China