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In many low-income countries, a significant portion of the population faces challenges in accessing quality healthcare. This study proposes an automated machine-learning approach to segment X-ray images, aiding medical personnel in identifying fractures. Medical facilities typically have their own image databases and X-ray machines. The objective is to classify digital X-ray images into five categories: elbow, leg, spinal cord, toes, and other items. This report presents the findings from assessing X-ray image identification for the Imaging CLEF-2019 challenge. The study utilizes advanced feature extraction and classification algorithms to categorize images, focusing on features that highlight bone size and shape. Techniques include edge detection, classification algorithms, and convolutional neural networks to enhance bone fracture detection based on bone morphology.

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Machine Learning-Based Classification of X-Ray Images Using Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Manisha Uttam Waghmare,
  • Vipul V. Bag,
  • Mithun B. Patil

摘要

In many low-income countries, a significant portion of the population faces challenges in accessing quality healthcare. This study proposes an automated machine-learning approach to segment X-ray images, aiding medical personnel in identifying fractures. Medical facilities typically have their own image databases and X-ray machines. The objective is to classify digital X-ray images into five categories: elbow, leg, spinal cord, toes, and other items. This report presents the findings from assessing X-ray image identification for the Imaging CLEF-2019 challenge. The study utilizes advanced feature extraction and classification algorithms to categorize images, focusing on features that highlight bone size and shape. Techniques include edge detection, classification algorithms, and convolutional neural networks to enhance bone fracture detection based on bone morphology.