基于颜色自相关图的乳腺肿瘤良恶性分类
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摘要
目的:构建一种乳腺肿瘤良恶性分类模型,使医生得到更加客观、准确的诊断结果。方法:借助BreaKHis数据集,提取乳腺肿瘤病理图像颜色自相关图的64维特征,利用k-NN分类器构建乳腺肿瘤良恶性分类模型,并对乳腺肿瘤良恶性进行分类。结果:颜色自相关图中像素空间距离d=1时分类精度最高,准确度平均达到87.01%,灵敏度平均达到88.52%,特异度平均达到85.49%。结论:该模型为乳腺肿瘤良恶性分类提供了一种新型的检测手段,可有效提高乳腺肿瘤良恶性临床诊断的准确率。
Objective To propose a classification model for benign and malignant breast tumors to improve their diagnoses.Methods The 64-dimension color auto-correlogram features of the breast tumor pathological images were extracted with BreaKHis dataset. A classification model was constructed by using k-NN classifier, and then was used for the classification of benign and malignant breast tumors. Results In the color auto-correlogram, the highest classification accuracy was obtained when the pixel space distance d was equal to 1. The mean values of the accuracy, sensitivity and specialty were 87.01%,88.52% and 85.49% respectively. Conclusion The model provides a new type of detection method for the benign and malignant classification of breast tumors and improves effectively the accuracy of clinical diagnosis. [Chinese Medical Equipment Journal,2019,40(6):13-15]
Objective To propose a classification model for benign and malignant breast tumors to improve their diagnoses.Methods The 64-dimension color auto-correlogram features of the breast tumor pathological images were extracted with BreaKHis dataset. A classification model was constructed by using k-NN classifier, and then was used for the classification of benign and malignant breast tumors. Results In the color auto-correlogram, the highest classification accuracy was obtained when the pixel space distance d was equal to 1. The mean values of the accuracy, sensitivity and specialty were 87.01%,88.52% and 85.49% respectively. Conclusion The model provides a new type of detection method for the benign and malignant classification of breast tumors and improves effectively the accuracy of clinical diagnosis. [Chinese Medical Equipment Journal,2019,40(6):13-15]
引文
[1]MCGUIRE S.World Cancer Report 2014.Geneva,Switzerland:World Health Organization,International Agency for Research on Cancer,WHO Press,2015[J].Adv Nutr,2016,7(2):418-419.
[2]SPANHOL F A,OLIVEIRA L S,PETITJEAN C,et al.A dataset for breast cancer histopathological image classification[J].IEEE Trans Biomed Eng,2016,63(7):1 455-1 462.
[3]OJALA T,PIETIKAINEN M,MAENPAAT.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Trans Pattern Anal Mach Intell,2002,24(7):971-987.
[4]HARALICK R M,SHANMUGAM K,DINSTEIN I.Textural features for image classification[J].IEEE Trans Syst,Man,Cybern,1973,SMC-3(6):610-621.
[5]CUTLER A,CUTLER D R,STEVENS J R.Random forests[J].Mach Learn,2004,45(1):157-176.
[6]CORTES C,VAPNIK V.Support-vector networks[J].Mach Learn,1995,20(3):273-297.
[7]王爽.宫颈细胞图像分割与识别算法研究[D].济南:山东科技大学,2017.
[8]CHEN F C.Back-propagation neural network for nonlinear self-tuning adaptive control[J].IEEE Control Syst Mag,1990,10(3):44-48.
[9]陈海蛟.基于纹理特征与ELM的肝癌识别方法的研究与实现[D].沈阳:东北大学,2015.
[10]HUANG G B,ZHU Q Y,SIEW C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1):489-501.
[11]HUANG J,KUMAR S R,MITRA M,et al.Spatial color indexing and applications[J].Int J Comput Vision,1999,35(3):245-268.
[12]裴新超.基于颜色和纹理特征聚类的图像检索[J].山西电子技术,2015(5):18-20,25.
[13]MIN R,STANLEY D A,YUAN Z,et al.A deep non-linear feature mapping for large-margin kNN classification[C]//2009Ninth IEEE International Conference on Data Mining,December 6-9,2009,Miami,Florida,USA.Washington:IEEE Computer Society,2009:357-366.
[2]SPANHOL F A,OLIVEIRA L S,PETITJEAN C,et al.A dataset for breast cancer histopathological image classification[J].IEEE Trans Biomed Eng,2016,63(7):1 455-1 462.
[3]OJALA T,PIETIKAINEN M,MAENPAAT.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Trans Pattern Anal Mach Intell,2002,24(7):971-987.
[4]HARALICK R M,SHANMUGAM K,DINSTEIN I.Textural features for image classification[J].IEEE Trans Syst,Man,Cybern,1973,SMC-3(6):610-621.
[5]CUTLER A,CUTLER D R,STEVENS J R.Random forests[J].Mach Learn,2004,45(1):157-176.
[6]CORTES C,VAPNIK V.Support-vector networks[J].Mach Learn,1995,20(3):273-297.
[7]王爽.宫颈细胞图像分割与识别算法研究[D].济南:山东科技大学,2017.
[8]CHEN F C.Back-propagation neural network for nonlinear self-tuning adaptive control[J].IEEE Control Syst Mag,1990,10(3):44-48.
[9]陈海蛟.基于纹理特征与ELM的肝癌识别方法的研究与实现[D].沈阳:东北大学,2015.
[10]HUANG G B,ZHU Q Y,SIEW C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1):489-501.
[11]HUANG J,KUMAR S R,MITRA M,et al.Spatial color indexing and applications[J].Int J Comput Vision,1999,35(3):245-268.
[12]裴新超.基于颜色和纹理特征聚类的图像检索[J].山西电子技术,2015(5):18-20,25.
[13]MIN R,STANLEY D A,YUAN Z,et al.A deep non-linear feature mapping for large-margin kNN classification[C]//2009Ninth IEEE International Conference on Data Mining,December 6-9,2009,Miami,Florida,USA.Washington:IEEE Computer Society,2009:357-366.