The probabilistic neural network(PNN) was applied in the recognition of cancerous stomach tissues.The characteristic FTIR peak frequencies(including vas(CH3),vs(CH2),δ(CH2),vas(PO2-),v(C-O),vs(PO2-) and vs(nucleic acid(DNA,RNA),cell proteins and membrance lipids) from corresponding stomach cancer tissues were used as the input vectors of the PNN neural network.The experimental results as follows: 1) when net parameter,spread,is 0.25～2.55,and the vas(PO2-),v(C-O),vs(PO2-) and vs(nucleic acid(DNA,RNA),cell proteins and membrance lipids) were employed as input vectors,PNN neural network exhibited the best performance with a mean accurate rate of recognition up to 96.2%;and when the spread between 0.2～4.60,and all the characteristic FTIR peak frequencies mentioned above were used as net input vectors,the PNN neural network exhibited a slightly lower performance with a mean accurate rate of recognition of 92.3%,while the PNN neural network performed very poorly when just v(C-O) and vs(PO2-) were used as input vectors with a mean accurate rate of recognition of 53.8%.2) In general,in comparison,PNN neural network should obviously be more excellent with respect to LVQ neural network(see ref.) in prediction and recognition of cancerous stomach tissues.
