Classification Enterococcus Faecium and Faecalis Bacteria Images using Bag of Features
DOI:
https://doi.org/10.29304/jqcm.2022.14.1.889Keywords:
Classification, Faecium, Faecalis, Bag of Features, ImagesAbstract
Many numerous genera of bacteria that have a foremost influence on human health. One of them is Enterococcus, which has numerous species excluding Faecium and Faecalis. It reasons many sicknesses such as the bowel inflammation bacteremia, urinary tract infection, wound inflammation and meningitis. In instruction to describe, the fitting antibiotic used to luxury these diseases, where the antibiotics use to inhibit Faecium bacteria different from Faecalis bacteria, it is necessary to determine the exact type of bacteria, especially that the two types Faecium and Faecalis are similar in shape to the extent large.
The target of this research is to classify Enterococcus bacteria into Faecium and Faecalis by using Bag of features technique, which accomplished and tested on database of the two species bacteria. The development method that combines circular shift pixel images, discrete Rigelet transform and Bag of features method which gives a better result on the non-stationary signals, which are executed on the above technique and newly trained classifier functional on blood agar plated images.
In biological methods, the biologists cultivate bacteria in blood agar plates to determine the type of bacteria where examined in the electron microscope by using the eyes to the dependence on the form of bacteria and often they need to re-implant the sample in other mediums in order to know the type of bacteria. This requires the cost of media and chemical materials and time. The method used in this research, the input for the program is blood agar plates images only and the type of bacteria is directly and accurately, this means that the method adopted in the program reduces costs and effort in biological methods, thus, this research helps biologists to be able to diagnose bacteria with less effort, cost and time.
The results exposed that the enriched classification method stretches better classification results on samples used images, when the average accuracy in the training set and validation set by using Bag of feature method are (0.83 and 0.79) respectively, they were increased in the proposed method to (1.00 and 0.92) respectively.
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