Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset
In this paper, a semi-automatic annotation of bacteria genera and species from DIBaS dataset is implemented using clustering and thresholding algorithms. A Deep learning model is trained to achieve the semantic segmentation and classification of the bacteria species. Classification accuracy of 95% i...
Gespeichert in:
Hauptverfasser: | , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | In this paper, a semi-automatic annotation of bacteria genera and species
from DIBaS dataset is implemented using clustering and thresholding algorithms.
A Deep learning model is trained to achieve the semantic segmentation and
classification of the bacteria species. Classification accuracy of 95% is
achieved. Deep learning models find tremendous applications in biomedical image
processing. Automatic segmentation of bacteria from gram-stained microscopic
images is essential to diagnose respiratory and urinary tract infections,
detect cancers, etc. Deep learning will aid the biologists to get reliable
results in less time. Additionally, a lot of human intervention can be reduced.
This work can be helpful to detect bacteria from urinary smear images, sputum
smear images, etc to diagnose urinary tract infections, tuberculosis,
pneumonia, etc. |
---|---|
DOI: | 10.48550/arxiv.2208.10737 |