Recognition of cancerous stomach tissues by artificial learning vector quantization neural network
Tong Yi-ping · Chemical Research and Application · 2006
The learning vector quantization(LVQ)neural network was applied in the recognition of cancerous stomach tissues.The characteristic FTIR peak frequencies(includingυ~(as)(CH_3)、υ~s(CH_2)、δ(CH_2)、υ~(as)(PO~-_2)、υ(C-O)、υ~s(PO~-_2)and V~s(nucleic acid(DNA,RNA),cell proteins and membrance lipids)from corresponding stomach cancer tissues were used as the input vectors of the LVQ neural network.The experimental results were given as follows:i)When the whole FTIR characteristic frequencies mentioned above were all employed as the input vectors,the mean accurate rate of recognition of the LVQ neural network was the best and resached to 89.3%,which indicated that the LVQ neural network was very satisfactory in the cancer recognition of stomach tissues and could be applied as an auxiliary medical diagnostic method;ii)The LVQ neural network would show higher mean accurate rate of recognition,in general,as more FTIR characteristic frequencies were emdployed as input vectors;iii)The mean accurate rate of recognition of the LVQ neural network would be different,as the input vectors comtained different kind of FTIR characteristic frequencies.