Browsing by Author "Kanabur, V.R."

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A Deep Learning Approach toÂ Enhance Semantic Segmentation ofÂ Bacteria andÂ Pus Cells fromÂ Microscopic Urine Smear Images Using Synthetic Data
(Springer Science and Business Media Deutschland GmbH, 2024) Kanabur, V.R.; Vijayasenan, D.; Sumam David, S.; Govindan, S.
Urine smear analysis aids in preliminary diagnosis of Urinary Tract Infection. But it is time-consuming and requires a lot of medical expertise. Automating the process using machine learning can save time and effort. However obtaining a large medical dataset is difficult due to data privacy concerns and medical expertise requirements. In this study, we propose a method to synthesize a large dataset of gram-stained microscopic images containing pus cells and bacteria. We train a machine learning model to achieve semantic segmentation of bacteria and pus cells using this dataset. Later we use it to perform transfer learning on a relatively small dataset of gram stained urine microscopic images. Our approach improved the F1-score from 50% to 63% for bacteria segmentation and from 77% to 83% for pus cell segmentation. This method has the potential to improve the turn-around time and the quality of preliminary diagnosis of Urinary Tract Infection. Â© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Adversarial Learning Based Semi-supervised Semantic Segmentation ofÂ Low Resolution Gram Stained Microscopic Images
(Springer Science and Business Media Deutschland GmbH, 2024) Singh, H.; Kanabur, V.R.; Sumam David, S.; Vijayasenan, D.; Govindan, S.
Urinary tract infections (UTIs) are infections that affect the urinary system. It is usually caused by bacteria and pus cells. Analyzing urine samples, including examining pus cells, is a standard method for diagnosing and monitoring UTIs. However, manually detecting bacteria or pus cells in microscopic urine images is a time-consuming and labour-intensive task for microbiologists. Therefore, the segmentation of microscopic pus cell images will ease the process of detecting UTI. Especially low resolution microscopic images are hard to annotate; therefore, in this study, we propose an adversarial learning based semi-supervised segmentation method for segmentation of pus cell images at low resolution i.e. 40Ã— using labeled high resolution images i.e. 100Ã—. The proposed methodology aims to ease the process of UTI detection by automating the segmentation of pus cell images. The results of the proposed methodology demonstrate an increase in the Dice coefficient score percentage by 1%, 1.6% and 2.4% on 40Ã— images when compared to fully supervised segmentation model trained on only 100Ã— data using three different architectures- Unet, ResUnet++, and PSPnet, respectively. Â© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset
(Institute of Electrical and Electronics Engineers Inc., 2023) Chethan Reddy, G.P.; Reddy, P.A.; Kanabur, V.R.; Vijayasenan, D.; Sumam David, S.; Govindan, S.
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. Pixel-level classification accuracy of 95 percent 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 cancer, 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. Â© 2023 IEEE.