In the paper, one multi-layer BP neural network is applied to identify metastasis malignancy tumor cells in lymph node puncture image. The topology structure of the network is as following: the node number of input-layer is 9, which involves morphologic features and chroma information; the node number of first hide-layer and second hide-layer is defined respectively as 8 and 12; the node number of output-layer is 3, which is the category number of recognized objects. Experimental results show that the learning performance of multi-layer BP neural network is good, comparing with three-layer BP neural network, the recognition rate is improved, and the method can be as an assistant means to recognize lymph node metastasis malignancy tumor cells.
By integrating Fuzzy Kohonen Clustering Network (FKCN) with Fuzzy Dempster-Shafer Evidential Reasoning Theory (FDSERT), a new multi-source data fusion of Remote Sensing information algorithm is proposed in this paper. The new algorithm can be applied in classification of remote sensing image through FKCN learning and FDSERT fusing. Experimental results comparing with the FKCN algorithm indicates that the classification algorithm of multi-source data fusion of Remote Sensing is superior to that of FKCN algorithm. And the algorithm can obviously improve classification accuracy. At the same time, the algorithm can makes the best of expert knowledge. Therefore the algorithm is an effective classification algorithm of Remote Sensing image.
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