Your browser doesn't support javascript.
loading
Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN.
IEEE J Biomed Health Inform ; 24(1): 160-170, 2020 01.
Article em En | MEDLINE | ID: mdl-30892256
Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and spectral information, provides more wealthy information for recognizing cells. In this paper, a novel blood cell classification framework, which combines a modulated Gabor wavelet and deep convolutional neural network (CNN) kernels, named as MGCNN, is proposed based on medical hyperspectral imaging. For each convolutional layer, multi-scale and orientation Gabor operators are taken dot product with initial CNN kernels. The essence is to transform the convolutional kernels into the frequency domain to learn features. By combining characteristics of Gabor wavelets, the features learned by modulated kernels at different frequencies and orientations are more representative and discriminative. Experimental results demonstrate that the proposed model can achieve better classification performance than traditional CNNs and widely used support vector machine approaches, especially as training small-sample-size situations.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Células Sanguíneas / Processamento de Imagem Assistida por Computador / Redes Neurais de Computação / Microscopia Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Células Sanguíneas / Processamento de Imagem Assistida por Computador / Redes Neurais de Computação / Microscopia Idioma: En Ano de publicação: 2020 Tipo de documento: Article