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- W3161553516 abstract "Aims: One way to detect the presence of pancreatic cancer is by examining it using Computed tomography (CT) scan. After a pancreatic cancer is detected, classification is done to determine the stage of cancer. In this study, we used the RNN model for the classification of Pancreatic cancer stadium. This study aimed to explain the procedure and the accuracy of the Elman tissue RNN modeling in pancreatic cancer stadium classification from the CT scan.Methods: The process carried out is to convert the image of red green blue (rgb) to a grayscale image on the CT scan data. After that the image was extracted with Gray Level Co-occurrence Matrix which was designed using Graphical User Interface with Matlab. There are 14 features, namely energy, contrast, correlation, Sum of Square, Inverse Different Moment, sum average, sum variance, sum entropy, entropy, differential variance, differential entropy, maximum probability, homogeneity, and dissimilarity. The feature is used as input, which is then divided into training data and testing data. After that, Elman network RNN modeling was carried out with data normalization, best model design, and data denormalization. The best model design was done by finding the number of hidden neurons and eliminating network inputs using the backpropagation algorithm.Results: The results of the best model training data and testing data were measured using sensitivity, specificity, and accuracy. So that from 74 training data obtained 92% accuracy rate, 96% sensitivity level as a reliable indicator when the results show pancreatic cancer, and 79% level of specificity as a good indicator when the results show normal pancreatic. While in 18 data testing showed 94% accuracy, 100% level of sensitivity, and 80% level of specificity.Conclusions: The conclusion in this study can be said that good classification results are obtained." @default.
- W3161553516 created "2021-05-24" @default.
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- W3161553516 date "2020-01-01" @default.
- W3161553516 modified "2023-09-24" @default.
- W3161553516 title "ISALPDC-16 : Classification of Pancreatic Cancer Stadium Using Recurrent Neural Network (RNN) Model Algorithm" @default.
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