Identification of Masses and Microcalcifications in the Mammograms Based on Three Neural Networks: Comparison and Discussion
Weidong Xu, Wei Liu, Lihua Li, Guoliang Shao, Juan Zhang · 2008
The incidence of breast cancer has been quickly raised in China recently. Mammography is regarded as the most reliable detection method of breast tumor and with the increase of cases, computer-aided diagnosis/detection (CAD) has been widely studied and applied to assist the radiologists. In the previous research, we had studied the detection algorithm of the microcalcifications (MCs) and the masses. And the identification on whether a focus is positive or negative is presented in this paper. In order to choose an effective classifier, three neural networks are used to identify the focuses respectively, and their performance is compared and then discussed. The experiments show that CMAC has very different performance for the training set and the testing set., ANFIS only performs well when the dimensionality of the features of the samples is comparatively low, while MLP is the most stable classifier, especially when the dimensionality of the features increases and the number of the samples is limited. And the final classification precision of the focuses is: for 120 mammograms of 60 cases, the true positive rate of the masses and the MCs are respectively 93.6% and 96.9%, and the false positive per image of them are 0.63 and 0.2.