计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 89-93.
池凯凯, 蔡荣辉, 丁维龙, 宦若虹, 毛科技
CHI Kai-kai, CAI Rong-hui, DING Wei-long, HUAN Ruo-hong, MAO Ke-ji
摘要: 儿童和青少年的骨龄表明了他们的生长发育情况。传统的骨龄评估方法是医生通过肉眼观察全左手的X光片中多块特定骨头的成熟程度,其精确性很依赖医生的主观判断能力,且评估较为费时。目前已经有基于全手掌骨图像且利用深度卷积神经网络进行骨龄自动评估的方法。为了提高骨龄识别的精度,文中提出从每个全手掌骨中分割出用于骨龄评估的14块特定骨头,然后对每块骨头训练出AlexNet卷积神经网络模型以进行骨成熟等级评估。另外,考虑到骨头发育是个连续过程,不同于传统的骨成熟等级判定,利用网络所输出的两个最可能等级的分类概率来计算骨头的加权得分。测试结果表明,该方法的平均骨龄误差为0.456岁,误差在1.0岁以内的准确率达到94.64%,显著优于基于全手掌骨图像的骨龄自动评估方法。
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