Comparative Evaluation of Transfer Learning Models for Detecting Malignant Cells in Urinary Cytology

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Best individual model (Xception): 88.57% sensitivity, 86.55% accuracy. Ensemble model: 95.41% sensitivity, 85.51% specificity, 92.10% accuracy

• Primary Outcome: Diagnostic accuracy of transfer learning models for malignant cell detection in urine cytology

• Nnt Or Sensitivity: Ensemble model sensitivity 95.41%, specificity 85.51%, positive predictive value 91.23%

• Confidence Interval: Not reported

Clinical Application

Requires significant computational infrastructure and technical expertise. Integration with existing cytology workflows unclear Potential application in Australian pathology laboratories for cytology screening, but would require TGA approval as medical device software. Cost-effectiveness analysis needed for PBS consideration Patients undergoing urine cytology screening for urothelial malignancy, particularly high-grade urothelial cell carcinoma

Abstract

AIMS AND OBJECTIVES: In the present paper, we compared the efficiency of six transfer learning models to detect malignant cells in urine cytology. We also applied an ensemble learning with weighted soft voting to assess its importance in the diagnostic accuracy in urine cytology. MATERIALS AND METHODS: The voided urine samples of total 104 cases of urothelial cell carcinoma (UCC) and 86 cases with no malignancy (benign) were selected. All the 104 cases of UCC were histopathology proven high grade urothelial cell carcinoma (UCC). Urine with negative cytology reports were followed up clinically. There were 446 images of benign samples and 1369 images from malignant samples on 100 x objective. We applied six transfer learning models (DenseNet121, inception_v3, ResNet50, MobileNetV2, VGG16 and Xception) to detect malignant cells in urine. To compare the performance of different models, dynamic training optimization was done and each model was auto stopped after their maximum performance. In addition, an ensemble learning with soft voting was used including the top three models to enhance the diagnostic accuracy. RESULT: Xception transfer model showed the highest sensitivity (88.57%), accuracy (86.55%), precision (80.52%) and FI score (84.35%). It was the best performing model. The other two top performing models were InceptionV3 and ResNet50. The area under curve of receiver operating characteristic (AUCROC) was ≤ 90 in all the transfer learning models. The accuracy, sensitivity, specificity, precision, F1-Score and AUCROC of the ensemble transfer learning model were as follows: 92.10%, 95.41%, 85.51%, 91.23%, 93.24% and 0.977 respectively. CONCLUSIONS: First time, we evaluated a large number of transfer learning models in urine cytology to detect malignant cells. All the models showed high sensitivity, specificity and accuracy. In addition, the ensemble learning technique with soft voting showed remarkable superior performance than individual top three transfer learning models. The techniques transfer learning and ensemble models have high potential to use in routine screening of urine.

References

1. 1.Dey, P., & Muralidaran, C. (2026). Comparative Evaluation of Transfer Learning Models for Detecting Malignant Cells in Urinary Cytology. Cytopathology, Article cyt.70061. https://doi.org/10.1111/cyt.70061