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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU141559" target="_blank" >RIV/00216305:26220/21:PU141559 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0010482521006569" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0010482521006569</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2021.104862" target="_blank" >10.1016/j.compbiomed.2021.104862</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

COMPUTERS IN BIOLOGY AND MEDICINE

ISSN

0010-4825

e-ISSN

1879-0534

Volume of the periodical

Aug. 2021

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

31

Pages from-to

1-31

UT code for WoS article

000703505200004

EID of the result in the Scopus database

2-s2.0-85114814299