Automated Classification of Urinary Cells: Using Convolutional Neural Network Pre-trained on Lung Cells

Atsushi Teramoto, Ayano Michiba, Yuka Kiriyama, Eiko Sakurai, Ryoichi Shiroki, Tetsuya Tsukamoto

Research output: Contribution to journalArticlepeer-review


Urine cytology, which is based on the examination of cellular images obtained from urine, is widely used for the diagnosis of bladder cancer. However, the diagnosis is sometimes difficult in highly heterogeneous carcinomas exhibiting weak cellular atypia. In this study, we propose a new deep learning method that utilizes image information from another organ for the automated classification of urinary cells. We first extracted 3137 images from 291 lung cytology specimens obtained from lung biopsies and trained a classification process for benign and malignant cells using VGG-16, a convolutional neural network (CNN). Subsequently, 1380 images were extracted from 123 urine cytology specimens and used to fine-tune the CNN that was pre-trained with lung cells. To confirm the effectiveness of the proposed method, we introduced three different CNN training methods and compared their classification performances. The evaluation results showed that the classification accuracy of the fine-tuned CNN based on the proposed method was 98.8% regarding sensitivity and 98.2% for specificity of malignant cells, which were higher than those of the CNN trained with only lung cells or only urinary cells. The evaluation results showed that urinary cells could be automatically classified with a high accuracy rate. These results suggest the possibility of building a versatile deep-learning model using cells from different organs.

Original languageEnglish
Article number1763
JournalApplied Sciences (Switzerland)
Issue number3
Publication statusPublished - 02-2023

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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