计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 254-259.doi: 10.11896/JsJkx.190700107
张华丽1, 康晓东1, 冉华2, 王亚鸽1, 李博1, 白放1
ZHANG Hua-li1, KANG Xiao-dong1, RAN Hua2, WANG Ya-ge1, LI Bo1 and BAI Fang1
摘要: 针对肺结节图像的分类识别精度和效率问题,分别将CNN(Convolution Neural Network)模型和DBN(Deep Belief Network)模型用于肺结节分类识别,并评估不同的深度学习模型在肺结节图像分类方面的性能。首先,实验将预处理过的训练集和标签分别输入到CNN模型和DBN模型,达到训练模型的目的;其次,将测试集输入到参数最优的模型中,比较两种模型测试集分类的准确率、敏感性和特异性,并分析两种模型的分类识别性能。最后,从分类准确率、敏感性和特异性3个指标以及时间复杂度来分析比较两种模型,发现CNN模型在肺结节图像分类识别上更有优越性。
中图分类号:
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