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作者:

Ibrahim, Adam M. (Ibrahim, Adam M..) | Hassan, Ayia A. (Hassan, Ayia A..) | Li, Jianqiang (Li, Jianqiang.) | Pei, Yan (Pei, Yan.)

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摘要:

The integration of deep learning (DL) and digital breast tomosynthesis (DBT) presents a unique opportunity to improve the reliability of breast cancer (BC) detection and diagnosis while accommodating novel imaging techniques. This study utilizes the publicly available Mammographic Image Analysis Society (MIAS) database v1.21 to evaluate DL algorithms in identifying and categorizing cancerous tissue. The dataset has undergone preprocessing and has been confirmed to be of exceptional quality. Transfer learning techniques are employed with three pre-trained models - MobileNet, Xception, DenseNet, and MobileNet LSTM - to improve performance on the target task. Stacking ensemble learning techniques will be utilized to combine the predictions of the best-performing models to make the final prediction for the presence of BC. The evaluation will measure the performance of each model using standard evaluation metrics, including accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S). This study highlights the potential of DL in enhancing diagnostic imaging and advancing healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词:

Learning systems Mammography Tomography Learning algorithms Diseases Long short-term memory

作者机构:

  • [ 1 ] [Ibrahim, Adam M.]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Hassan, Ayia A.]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Li, Jianqiang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Pei, Yan]Computer Science Division, University of Aizu, Fukushima, Aizuwakamatsu; 965-8580, Japan

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ISSN: 1876-1100

年份: 2024

卷: 1134

页码: 181-192

语种: 英文

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SCOPUS被引频次: 1

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