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Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
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ResNet-SCDA-50 for Breast Abnormality Classification.

Xiang Yu, Cheng Kang, David S Guttery

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 15, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an AI system using ResNet-50 and a novel data augmentation technique (SCDA) for accurate breast cancer detection from mammograms. The AI achieved high accuracy, demonstrating its effectiveness in classifying breast abnormalities.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Breast cancer is a leading global cancer in women, necessitating advanced diagnostic tools.
    • Artificial intelligence (AI) and deep learning show promise in enhancing early breast cancer detection accuracy.
    • Mammography remains a cornerstone of clinical breast screening.

    Purpose of the Study:

    • To develop and evaluate an AI-powered diagnostic system for classifying breast abnormalities from mammographic images.
    • To introduce and validate a novel data augmentation framework, SCDA, for improving deep learning model performance in breast cancer screening.
    • To assess the efficacy of the ResNet-SCDA-50 model in distinguishing between normal and abnormal regions in mammograms.

    Main Methods:

    • A ResNet-50 deep learning architecture was employed for image classification.
    • A new data augmentation framework, Scaling and Contrast limited adaptive histogram equalization Data Augmentation (SCDA), was developed.
    • The SCDA framework involves scaling images and applying Contrast Limited Adaptive Histogram Equalization (CLAHE), followed by combining augmented and original datasets.
    • The system was trained and tested on mammographic datasets from INbreast and MINI-MIAS for binary classification of masses and microcalcifications.

    Main Results:

    • The study presents the first use of image contrast enhancement as a data augmentation technique.
    • The ResNet-SCDA-50 model achieved an average specificity of 98.55% and sensitivity of 92.83%.
    • The best-performing model demonstrated an overall accuracy of 95.74% in classifying breast abnormalities.

    Conclusions:

    • The proposed AI system, incorporating the SCDA data augmentation method, is highly effective for breast abnormality classification.
    • The novel SCDA technique significantly enhances the performance of deep learning models in mammographic analysis.
    • This AI approach offers a promising tool for improving the accuracy and efficiency of breast cancer screening.