{'fraction_non_alphanumeric': 0.022222222222222223, 'fraction_numerical': 0.0, 'mean_word_length': 5.391891891891892, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Fuzzy-Based Learning Vector Quantization Neural Network for Recurrent Nasal Papilloma Detection

The objective of this paper is to develop a complete solution for recurrent nasal papilloma (RNP) detection. Recently, Gadolinium-enhanced dynamic magnetic resonance imaging (MRI) has been developed and widely used in the clinical diagnosis of RNP. Because the response of RNP regions in Gadolinium-enhanced MR images is different from the response of normal tissues, the difference between the dynamic-MR images before and after administering a contrast material can be used to extract coarse RNP regions automatically. In this study, a fuzzy algorithm for learning vector quantization neural network is used to pick suspicious RNP regions. Finally, a feature-based region growing method is applied to recover complete RNP regions. The experimental results show that the proposed method can detect RNP regions automatically, correctly, and fast.