Marina Gorunescu

TL;DR: EUS elastography is a promising method that allows characterization and differentiation of normal pancreas, chronic pancreatitis, and pancreatic cancer and enabled an optimal prediction of the types of pancreatic lesions.

TL;DR: The results from this study indicate that the Equal Error Rate (EER) is significantly influenced by the attribute selection process and to a lesser extent on the authentication algorithm employed, and provides evidence that a Probabilistic Neural Network (PNN) can be superior in terms of reduced training time and classification accuracy when compared with a typical MLFN back-propagation trained neural network.

TL;DR: The result clearly showed that the best performance was obtained by the cluster network, followed by SOM and k-means, their predicting accuracy ranging from 62% to 78%.

TL;DR: A tandem feature selection mechanism and evolutionary-driven neural network as a computer-based support for liver fibrosis stadialization in chronic hepatitis C is proposed and the efficiency of the proposed intelligent system in comparison with other machine learning techniques reported in literature is demonstrated.

TL;DR: A hybrid approach utilizing rough sets and a probabilistic neural network to perform supervised classification of biomedical dataset results indicate that rough sets were able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy.