Bone-Fracture-Detection:X射线图像中的骨骨折检测
骨骨折检测 :worried_face:
X射线图像中用于骨折检测的数据扩充和预处理 :person_raising_hand:
1. radius骨远端骨折 :crying_face:
1.1引言 :grinning_face_with_big_eyes:
这部分是关于使用更快的RCNN来检测远侧X射线图像中的远端识别并定位远端radius骨骨折。 （38张图像-分辨率高达1600×1600像素用于训练）。 结果（ACC = 0.96和mAP = 0.866）比医生和放射线医师（仅0.7 ACC）获得的检测结果准确得多。
一些挑战： :grinning_face_with_big_eyes:
在许多情况下，裂缝的尺寸很小且难以检测。
骨折有各种各样的不同形状
Faster R-CNN的优势在于它可以处理高分辨率图像。 同样，可以在检测少量图像的对象时以较高的精度训练Faster R-CNN。 两个明确的任务：
分类远端radius骨是否骨折。
找到骨折的位置。
1.2更快的RCNN
更快的RCNN包含3个部分：
用于分类和生成特征图的卷积深度神经网络。
区域提案
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