计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 260-267.doi: 10.11896/JsJkx.191200011
古万荣1, 樊纬江1, 谢贤芬2, 张子烨3, 毛宜军1, 梁早清1, 林镇溪1
GU Wan-rong1, FAN Wei-Jiang1, XIE Xian-fen2, ZHANG Zi-ye3, MAO Yi-Jun1, LIANG Zao-qing1 and LIN Zhen-xi1
摘要： 随着计算机视觉识别技术的发展,越来越多的研究人员将该技术应用到肿瘤图像的识别上。但由于成本,许多医院仍然采用成本较低的B超等设备,产生了模糊、伪影和多个相似肿瘤噪声区域。目前的方法在清晰图像识别中具有很高的精度,但在超声图像方面却存在低准确度且不稳定的结果,其原因是许多现有算法对模糊、噪声图像误判较高。文中基于R-CNN和PRN的方法快速准确地获取高噪声的超声图像的关键特征,并通过数据增强和形态学滤波的方法确保了识别的稳定性。同时,所提方法还融合了血流信号分类模型增强识别精度。基于实际甲状腺肿瘤图像的数据集可知,提出的方法对比新近算法具有较高的准确性和稳定性。
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