Detail publikace

Automated Detection of Bioimages using Novel Deep Feature Fusion Algorithm and An Effective High-Dimensional Feature Selection Approach

Joshi, C., J. Mishra, R. Gandhi, P. Pathak, V., K. Burget, R. Dutta M.K.

Originální název

Automated Detection of Bioimages using Novel Deep Feature Fusion Algorithm and An Effective High-Dimensional Feature Selection Approach

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Convolutional Neural Networks; Bioimage Classification; Transfer Learning; Evolutionary Algorithms; Feature Fusion; Pre-trained CNNs

Autoři

Joshi, C., J.; Mishra, R.; Gandhi, P.; Pathak, V., K.; Burget, R.; Dutta M.K.

Vydáno

10. 9. 2021

Nakladatel

Computers in Biology and Medicine

ISSN

0010-4825

Periodikum

COMPUTERS IN BIOLOGY AND MEDICINE

Ročník

Aug. 2021

Číslo

8

Stát

Spojené státy americké

Strany od

1

Strany do

31

Strany počet

31

URL

https://www.sciencedirect.com/science/article/pii/S0010482521006569

BibTex

@article{BUT172463,
  author="Joshi, C., J. and Mishra, R. and Gandhi, P. and Pathak, V., K. and Burget, R. and Dutta M.K.",
  title="Automated Detection of Bioimages using Novel Deep Feature Fusion Algorithm and An Effective High-Dimensional Feature Selection Approach",
  journal="COMPUTERS IN BIOLOGY AND MEDICINE",
  year="2021",
  volume="Aug. 2021",
  number="8",
  pages="1--31",
  doi="10.1016/j.compbiomed.2021.104862",
  issn="0010-4825",
  url="https://www.sciencedirect.com/science/article/pii/S0010482521006569"
}