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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

COMPUTERS IN BIOLOGY AND MEDICINE

ISSN

0010-4825

e-ISSN

1879-0534

Svazek periodika

Aug. 2021

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

31

Strana od-do

1-31

Kód UT WoS článku

000703505200004

EID výsledku v databázi Scopus

2-s2.0-85114814299