Brain tumor demarcation by applying a LAMSTAR neural network to spectroscopy data

The application of optical spectroscopy for intra-operatively delineating brain tumors has been studied in this paper. The classification of tissue as normal, tumor and boundary is done using the LAMSTAR neural network (NN). The objective is to combine both fluorescence and reflectance as attributes to be used for the demarcation, thus giving the identification greater specificity and sensitivity. The input word has seven sub-words, five with autofluorescence parameters and two with reflectance values. The mean and standard deviation for the fluorescence parameters that were used for setting the weights of the NN were obtained from previous work. The reflectance value was used with the fluorescence parameters through a two-step discrimination algorithm. The neural network was trained with 10 sets of each tumor, normal and boundary type of tissue parameters. The network was then tested with 15 complete input sets and 10 incomplete sets for the identification. A 100% success rate was obtained for the complete testing sets and 80% for the incomplete ones. The most significant self-organizing map layers of the network were also identified for each decision. A sensitivity of 97.1% and specificity of 94.73% were achieved, which is much higher than earlier published results of 89 and 76%, respectively.