TY - GEN
T1 - A Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear Images Using Synthetic Data
AU - Kanabur, Vidyashree R.
AU - Vijayasenan, Deepu
AU - S, Sumam David
AU - Govindan, Sreejith
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85200326812
UR - https://www.scopus.com/pages/publications/85200326812#tab=citedBy
U2 - 10.1007/978-3-031-58181-6_21
DO - 10.1007/978-3-031-58181-6_21
M3 - Conference contribution
AN - SCOPUS:85200326812
SN - 9783031581809
T3 - Communications in Computer and Information Science
SP - 244
EP - 255
BT - Computer Vision and Image Processing - 8th International Conference, CVIP 2023, Revised Selected Papers
A2 - Kaur, Harkeerat
A2 - Jakhetiya, Vinit
A2 - Goyal, Puneet
A2 - Khanna, Pritee
A2 - Raman, Balasubramanian
A2 - Kumar, Sanjeev
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Conference on Computer Vision and Image Processing, CVIP 2023
Y2 - 3 November 2023 through 5 November 2023
ER -
