Kanabur, V.R.Vijayasenan, D.Sumam David, S.Govindan, S.2026-02-062024Communications in Computer and Information Science, 2024, Vol.2009 CCIS, , p. 244-25518650929https://doi.org/10.1007/978-3-031-58181-6_21https://idr.nitk.ac.in/handle/123456789/29010Urine 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Machine learningSynthetic dataTransfer learning