Ensemble Learning Model: A Novel Technique to Detect Malignancy in Effusion Cytology.
Journal:
Cytopathology : official journal of the British Society for Clinical Cytology
Published Date:
Nov 8, 2025
Abstract
AIMS AND OBJECTIVES: This study applied an ensemble learning model combining six transfer learning architectures to detect malignancy in effusion cytology. MATERIALS AND METHODS: In this current study, we had a total of 110 cases of effusion cytology consisting of 59 benign and 51 malignant cases. We took a total of 755 representative microphotographs from the Papanicolaou's stained smear. The ensemble learning model consists of DenseNet121, Xception, ResNet50, MobileNetV2, InceptionV3, and VGG16 with a soft voting technique. After initial feature extraction, fine-tuning was performed by unfreezing the final layers of each backbone. The neural network was implemented in Jupyter Notebook. RESULT: The model achieved sensitivity, specificity, accuracy, precision, negative predictive value, F1 score, and AUROC of 0.92, 0.89, 0.90, 0.89, 0.92, 0.91, and 0.96, respectively. CONCLUSIONS: To our knowledge, this is the first study applying a six-model ensemble deep learning approach in effusion cytology. The combined transfer learning framework demonstrated excellent diagnostic performance and may serve as a future tool for carcinoma detection in effusion cytology.
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