<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In microbiology, computer methods are applied in the analysis and recognition of laboratory-acquired microscopic images concerning, for example, bacterial cells or other microorganisms. Proper recognition of the species and genera of bacteria is a key stage in the microbiological diagnostics process, because it allows a quick start of the appropriate therapy. The original method proposed in the paper concerns the automatic recognition of selected species and genera of bacteria presented in digital images. The classification was made on the basis of the analysis of the physical characteristics of bacterial cells using the product of classifier confidence weights. The end result of the classification process is the classification list, sorted in descending order according to the weights of the classifiers. In addition to the correct classification, a list of other possible results of the analysis is obtained. The method thus allows not only the classification, but also an analysis of the confidence level of the selection made. The proposed method can be used to recognize not only bacterial cells, but also other microorganisms, for example, fungi that exhibit similar morphological characteristics. In addition, the use of the method does not require the application of specialized computer equipment, which widens the scope of applications regardless of the laboratory IT infrastructure, not only in microbiological diagnostics, but also in other diagnostic laboratories.</p></abstract>

In microbiology, computer methods are applied in the analysis and recognition of laboratory-acquired microscopic images concerning, for example, bacterial cells or other microorganisms. Proper recognition of the species and genera of bacteria is a key stage in the microbiological diagnostics process, because it allows a quick start of the appropriate therapy. The original method proposed in the paper concerns the automatic recognition of selected species and genera of bacteria presented in digital images. The classification was made on the basis of the analysis of the physical characteristics of bacterial cells using the product of classifier confidence weights. The end result of the classification process is the classification list, sorted in descending order according to the weights of the classifiers. In addition to the correct classification, a list of other possible results of the analysis is obtained. The method thus allows not only the classification, but also an analysis of the confidence level of the selection made. The proposed method can be used to recognize not only bacterial cells, but also other microorganisms, for example, fungi that exhibit similar morphological characteristics. In addition, the use of the method does not require the application of specialized computer equipment, which widens the scope of applications regardless of the laboratory IT infrastructure, not only in microbiological diagnostics, but also in other diagnostic laboratories.

Recognition of Species and Genera of Bacteria By Means of the Product of Weights of the Classifiers

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In microbiology, computer methods are applied in the analysis and recognition of laboratory-acquired microscopic images concerning, for example, bacterial...

Recognition of Species and Genera of Bacteria By Means of the Product of Weights of the Classifiers

Published Online: Sep 29, 2020

Page range: 463 - 473

Received: Dec 05, 2019

Accepted: May 29, 2020

DOI: https://doi.org/10.34768/amcs-2020-0034

Keywords
pattern recognition, recognition of bacterial cells, classifiers, product of weights of the classifiers

© 2020 Anna Plichta, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Recognition of Species and Genera of Bacteria By Means of the Product of Weights of the Classifiers

Published Online: Sep 29, 2020

Page range: 463 - 473

Received: Dec 05, 2019

Accepted: May 29, 2020

DOI: https://doi.org/10.34768/amcs-2020-0034

Keywordspattern recognition, recognition of bacterial cells, classifiers, product of weights of the classifiers

© 2020 Anna Plichta, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
pattern recognition, recognition of bacterial cells, classifiers, product of weights of the classifiers