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Automated detection of bioimages using novel deep feature fusion algorithm and effective high-dimensional feature selection approach.
Maurya, Ritesh; Pathak, Vinay Kumar; Burget, Radim; Dutta, Malay Kishore.
Afiliação
  • Maurya R; Centre for Advanced Studies, Dr A.P.J. Abdul Kalam Technical University, Lucknow, India. Electronic address: ritesh@cas.res.in.
  • Pathak VK; Dr A.P.J. Abdul Kalam Technical University, Lucknow, India. Electronic address: vinay@vpathak.in.
  • Burget R; Department of Telecommunications, Faculty of Electrical Engineering and Communication, BRNO University of Technology, Czech Republic. Electronic address: burgetrm@feec.vutbr.cz.
  • Dutta MK; Centre for Advanced Studies, Dr A.P.J. Abdul Kalam Technical University, Lucknow, India. Electronic address: mkd@cas.res.in.
Comput Biol Med ; 137: 104862, 2021 10.
Article em En | MEDLINE | ID: mdl-34534793
ABSTRACT
The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Redes Neurais de Computação Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Redes Neurais de Computação Idioma: En Ano de publicação: 2021 Tipo de documento: Article