Fuzzy Model for the Automatic Recognition of Human Dendritic Cells
<b>Background and objective:</b> Nowadays, foodborne illness is considered one of the most outgrowing diseases in the world, and studies show that its rate increases sharply each year. Foodborne illness is considered a public health problem which is caused by numerous factors, such as fo...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/1/13 |
| _version_ | 1797440477685874688 |
|---|---|
| author | Marwa Braiki Kamal Nasreddine Abdesslam Benzinou Nolwenn Hymery |
| author_facet | Marwa Braiki Kamal Nasreddine Abdesslam Benzinou Nolwenn Hymery |
| author_sort | Marwa Braiki |
| collection | DOAJ |
| description | <b>Background and objective:</b> Nowadays, foodborne illness is considered one of the most outgrowing diseases in the world, and studies show that its rate increases sharply each year. Foodborne illness is considered a public health problem which is caused by numerous factors, such as food intoxications, allergies, intolerances, etc. Mycotoxin is one of the food contaminants which is caused by various species of molds (or fungi), which, in turn, causes intoxications that can be chronic or acute. Thus, even low concentrations of Mycotoxin have a severely harmful impact on human health. It is, therefore, necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, researchers have approved a new method of investigation using human dendritic cells, yet the analysis of the geometric properties of these cells is still visual. Moreover, this type of analysis is subjective, time-consuming, and difficult to perform manually. In this paper, we address the automation of this evaluation using image-processing techniques. <b>Methods:</b> Automatic classification approaches of microscopic dendritic cell images are developed to provide a fast and objective evaluation. The first proposed classifier is based on support vector machines (SVM) and Fisher’s linear discriminant analysis (FLD) method. The FLD–SVM classifier does not provide satisfactory results due to the significant confusion between the inhibited cells on one hand, and the other two cell types (mature and immature) on the other hand. Then, another strategy was suggested to enhance dendritic cell recognition results that are emitted from microscopic images. This strategy is mainly based on fuzzy logic which allows us to consider the uncertainties and inaccuracies of the given data. <b>Results:</b> These proposed methods are tested on a real dataset consisting of 421 images of microscopic dendritic cells, where the fuzzy classification scheme efficiently improved the classification results by successfully classifying 96.77% of the dendritic cells. <b>Conclusions:</b> The fuzzy classification-based tools provide cell maturity and inhibition rates which help biologists evaluate severe health impacts caused by food contaminants. |
| first_indexed | 2024-03-09T12:08:45Z |
| format | Article |
| id | doaj.art-803ff295a11b4e189de402a70cbb5dae |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T12:08:45Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-803ff295a11b4e189de402a70cbb5dae2023-11-30T22:55:29ZengMDPI AGJournal of Imaging2313-433X2023-01-01911310.3390/jimaging9010013Fuzzy Model for the Automatic Recognition of Human Dendritic CellsMarwa Braiki0Kamal Nasreddine1Abdesslam Benzinou2Nolwenn Hymery3ENIB, UMR CNRS 6285 LabSTICC, 29238 Brest, FranceENIB, UMR CNRS 6285 LabSTICC, 29238 Brest, FranceENIB, UMR CNRS 6285 LabSTICC, 29238 Brest, FranceUniv Brest, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, 29280 Plouzané, France<b>Background and objective:</b> Nowadays, foodborne illness is considered one of the most outgrowing diseases in the world, and studies show that its rate increases sharply each year. Foodborne illness is considered a public health problem which is caused by numerous factors, such as food intoxications, allergies, intolerances, etc. Mycotoxin is one of the food contaminants which is caused by various species of molds (or fungi), which, in turn, causes intoxications that can be chronic or acute. Thus, even low concentrations of Mycotoxin have a severely harmful impact on human health. It is, therefore, necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, researchers have approved a new method of investigation using human dendritic cells, yet the analysis of the geometric properties of these cells is still visual. Moreover, this type of analysis is subjective, time-consuming, and difficult to perform manually. In this paper, we address the automation of this evaluation using image-processing techniques. <b>Methods:</b> Automatic classification approaches of microscopic dendritic cell images are developed to provide a fast and objective evaluation. The first proposed classifier is based on support vector machines (SVM) and Fisher’s linear discriminant analysis (FLD) method. The FLD–SVM classifier does not provide satisfactory results due to the significant confusion between the inhibited cells on one hand, and the other two cell types (mature and immature) on the other hand. Then, another strategy was suggested to enhance dendritic cell recognition results that are emitted from microscopic images. This strategy is mainly based on fuzzy logic which allows us to consider the uncertainties and inaccuracies of the given data. <b>Results:</b> These proposed methods are tested on a real dataset consisting of 421 images of microscopic dendritic cells, where the fuzzy classification scheme efficiently improved the classification results by successfully classifying 96.77% of the dendritic cells. <b>Conclusions:</b> The fuzzy classification-based tools provide cell maturity and inhibition rates which help biologists evaluate severe health impacts caused by food contaminants.https://www.mdpi.com/2313-433X/9/1/13classificationdendritic cellsmorphologySVMFLDfuzzy logic |
| spellingShingle | Marwa Braiki Kamal Nasreddine Abdesslam Benzinou Nolwenn Hymery Fuzzy Model for the Automatic Recognition of Human Dendritic Cells Journal of Imaging classification dendritic cells morphology SVM FLD fuzzy logic |
| title | Fuzzy Model for the Automatic Recognition of Human Dendritic Cells |
| title_full | Fuzzy Model for the Automatic Recognition of Human Dendritic Cells |
| title_fullStr | Fuzzy Model for the Automatic Recognition of Human Dendritic Cells |
| title_full_unstemmed | Fuzzy Model for the Automatic Recognition of Human Dendritic Cells |
| title_short | Fuzzy Model for the Automatic Recognition of Human Dendritic Cells |
| title_sort | fuzzy model for the automatic recognition of human dendritic cells |
| topic | classification dendritic cells morphology SVM FLD fuzzy logic |
| url | https://www.mdpi.com/2313-433X/9/1/13 |
| work_keys_str_mv | AT marwabraiki fuzzymodelfortheautomaticrecognitionofhumandendriticcells AT kamalnasreddine fuzzymodelfortheautomaticrecognitionofhumandendriticcells AT abdesslambenzinou fuzzymodelfortheautomaticrecognitionofhumandendriticcells AT nolwennhymery fuzzymodelfortheautomaticrecognitionofhumandendriticcells |