Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset
This work addresses the need for faster and more reliable diagnosis of infections like urinary tract infections and tuberculosis in biomedical imaging, though it appears incremental as it applies existing deep learning methods to a specific dataset.
The paper tackles the problem of identifying bacteria genera and species from gram-stained microscopic images by implementing a semi-automatic annotation method using clustering and thresholding, then training a deep learning model for semantic segmentation and classification, achieving 95% accuracy.
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.