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This paper employs the digital database for screening mammography (DDSM) to refine algorithms for detecting breast calcifications using PySpark, a framework built on Apache Spark. By integrating big data analytics and machine learning techniques, we enhance computational efficiency and detection accuracy for benign, benign without callback, and malignant cases. Our results indicate that leveraging PySpark’s distributed computing capabilities significantly improves the speed and precision of the detection algorithms, offering promising directions for future research in medical imaging technology.

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Optimizing Breast Calcification Detection in Mammography Using PySpark: A Big Data and Machine Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Ismail Lamaakal,
  • Zakaria Charroud,
  • Yassine Maleh,
  • Ibrahim Ouahbi,
  • Khalid El Makkaoui

摘要

This paper employs the digital database for screening mammography (DDSM) to refine algorithms for detecting breast calcifications using PySpark, a framework built on Apache Spark. By integrating big data analytics and machine learning techniques, we enhance computational efficiency and detection accuracy for benign, benign without callback, and malignant cases. Our results indicate that leveraging PySpark’s distributed computing capabilities significantly improves the speed and precision of the detection algorithms, offering promising directions for future research in medical imaging technology.