Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset

G. P. Chethan Reddy; Pullagurla Abhijith Reddy; Vidyashree R. Kanabur; Deepu Vijayasenan; S. Sumam David; Sreejith Govindan

doi:10.1109/ICCSC56913.2023.10142976

Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset

G. P. Chethan Reddy
, Pullagurla Abhijith Reddy
, Vidyashree R. Kanabur
, Deepu Vijayasenan
, S. Sumam David
, Sreejith Govindan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665493932
DOIs	https://doi.org/10.1109/ICCSC56913.2023.10142976
Publication status	Published - 2023
Event	2nd International Conference on Computational Systems and Communication, ICCSC 2023 - Thiruvananthapuram, India Duration: 03-03-2023 → 04-03-2023

Publication series

Name	ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication

Conference

Conference	2nd International Conference on Computational Systems and Communication, ICCSC 2023
Country/Territory	India
City	Thiruvananthapuram
Period	03-03-23 → 04-03-23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Vision and Pattern Recognition
Information Systems
Information Systems and Management
Computational Mathematics
Health Informatics

Access to Document

10.1109/ICCSC56913.2023.10142976

Cite this

Chethan Reddy, G. P., Reddy, P. A., Kanabur, V. R., Vijayasenan, D., Sumam David, S., & Govindan, S. (2023). Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset. In ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication (ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCSC56913.2023.10142976

Chethan Reddy, G. P. ; Reddy, Pullagurla Abhijith ; Kanabur, Vidyashree R. et al. / Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset. ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication. Institute of Electrical and Electronics Engineers Inc., 2023. (ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication).

@inproceedings{aeea7d3c1651474a8a772a9b748dc824,

title = "Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset",

abstract = "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.",

author = "\{Chethan Reddy\}, \{G. P.\} and Reddy, \{Pullagurla Abhijith\} and Kanabur, \{Vidyashree R.\} and Deepu Vijayasenan and \{Sumam David\}, S. and Sreejith Govindan",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd International Conference on Computational Systems and Communication, ICCSC 2023 ; Conference date: 03-03-2023 Through 04-03-2023",

year = "2023",

doi = "10.1109/ICCSC56913.2023.10142976",

language = "English",

series = "ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication",

address = "United States",

}

Chethan Reddy, GP, Reddy, PA, Kanabur, VR, Vijayasenan, D, Sumam David, S & Govindan, S 2023, Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset. in ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication. ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Computational Systems and Communication, ICCSC 2023, Thiruvananthapuram, India, 03-03-23. https://doi.org/10.1109/ICCSC56913.2023.10142976

Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset. / Chethan Reddy, G. P.; Reddy, Pullagurla Abhijith; Kanabur, Vidyashree R. et al.
ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication. Institute of Electrical and Electronics Engineers Inc., 2023. (ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset

AU - Chethan Reddy, G. P.

AU - Reddy, Pullagurla Abhijith

AU - Kanabur, Vidyashree R.

AU - Vijayasenan, Deepu

AU - Sumam David, S.

AU - Govindan, Sreejith

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85162633447

T3 - ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication

BT - ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Computational Systems and Communication, ICCSC 2023

Y2 - 3 March 2023 through 4 March 2023

ER -

Chethan Reddy GP, Reddy PA, Kanabur VR, Vijayasenan D, Sumam David S, Govindan S. Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset. In ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication. Institute of Electrical and Electronics Engineers Inc. 2023. (ICCSC 2023 - Proceedings of the 2nd International Conference on Computational Systems and Communication). doi: 10.1109/ICCSC56913.2023.10142976

Semi-Automatic Labeling and Semantic Segmentation of Gram-Stained Microscopic Images from DIBaS Dataset

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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Cite this