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Detection of Metastatic Tissues in Histopathologic Images using DenseNet-121 with Data Augmentation.

Shereen Afifi, Ranpreet Kaur, Hamid GholamHosseini

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    This study introduces DenseNet-121 for automated metastatic tissue detection in histopathology scans. The model achieved high accuracy and F1-score, outperforming other networks and aiding cancer diagnosis.

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    Area of Science:

    • Digital Pathology
    • Computational Pathology
    • Medical Image Analysis

    Background:

    • Accurate identification of metastatic tissues in histopathological scans is crucial for effective cancer detection and treatment planning.
    • Manual analysis of these scans can be time-consuming and prone to inter-observer variability.
    • Automating this process can enhance diagnostic efficiency and reliability.

    Purpose of the Study:

    • To develop and evaluate an automated system for detecting metastatic tissue in histopathological images.
    • To compare the performance of the DenseNet-121 model against other convolutional neural networks (CNNs) for this task.
    • To assess the model's generalization capabilities on unseen data.

    Main Methods:

    • Utilized the CAMELYON17 dataset for training and validation of the DenseNet-121 model.
    • Employed data augmentation techniques to improve model generalization and robustness.
    • Compared DenseNet-121 performance against ResNet-18 and ResNet-50 architectures.
    • Evaluated model performance using accuracy and F1-score metrics.
    • Tested the model's efficacy on the CAMELYON16 dataset to assess performance on previously unseen images.

    Main Results:

    • DenseNet-121 with data augmentation achieved a testing accuracy of 0.98 and an F1-score of 0.98.
    • The DenseNet-121 model demonstrated superior performance compared to ResNet-18 and ResNet-50.
    • The model maintained high accuracy on the CAMELYON16 dataset, indicating good generalization.
    • Achieved state-of-the-art results in metastatic tissue detection.

    Conclusions:

    • DenseNet-121 is a highly effective deep learning model for automated metastatic tissue detection in histopathology.
    • The proposed method shows significant potential as an assistive tool for pathologists.
    • This technology can accelerate cancer diagnosis and improve the overall reliability of pathological assessments.