Abstract
Abstract: Two contour-based fracture detection strategies are presented in the ensuing research. The programs are designed to help medical practitioners identify human long bone fractures from X-ray pictures. The line-based fracture detection systems suggested in [1] served as the foundation for the creation of the contour-based fracture. Convolutional Neural Networks (CNN) are widely used in current Computer Aided Diagnosis (CAD) systems to classify broken X-ray images. Even while the current CAD systems achieve excellent accuracy, doing so comes at a cost: a large volume of training data. By using identified contours in X-ray pictures, the suggested techniques aim to achieve high classification accuracy with little training data. Two methods exist for contour-based fracture detection. The first is the enhanced CHFB scheme, while the second is the Standard Contour Histogram Feature-Based (CHFB). The modified CHFB scheme differs from the other one in that it removes the detected flesh contours surrounding the leg region.
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