ArticleFrontiers in digital health2026
Ensemble based in transfer learning for cytological classification in pleural fluid.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pleural effusion cytology is critical for diagnosing benign and malignant conditions, yet manual interpretation remains time-consuming and prone to subjectivity. The increasing burden of malignant pleural effusion in resource-constrained settings highlights the need for automated diagnostic solutions. This study presents an ensemble deep learning framework combining ResNet50V2, DenseNet121, and InceptionV3 architectures with transfer learning for classifying pleural cytology images into negative for malignancy (NFM) and malignant (MAL) categories. Three scenarios were evaluated: no data augmentation, 50% augmentation, and 300% augmentation. A local dataset of 1,292 images from Hospital Nacional Cayetano Heredia and an external Kaggle dataset (693 images) were used for training, validation, and independent testing. Performance was measured using accuracy, precision, recall, and
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