{'fraction_non_alphanumeric': 0.019801980198019802, 'fraction_numerical': 0.009900990099009901, 'mean_word_length': 5.452127659574468, '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}}
Pulmonary Nodules Detection Based on CNN Multi-scale Feature Fusion

Early diagnosis of lung cancer can effectively reduce the mortality, lung nodules is an early manifestation of lung cancer, so the use of computer aided detection of lung nodules has been rapidly developed, which quickly and accurately locate the exact location of lung nodules is a challenging work. In this paper, a two-dimensional convolutional neural network (CNN) based pulmonary nodules detection framework is proposed. The detection framework consists of two parts: region proposal generation and false positive reduction. First, take the first 10 layers of VGG16 as the basic feature extraction network, and select three feature layers in the last several layers of VGG16 network to generate candidate anchors. Secondly, deconvolution is added between feature layers to fuse higher-order feature information and further generate object categories and precise object locations and sizes, and the final result was selected through non-maximum suppression to reduce false positives. Experiments on LUNA16 dataset show that the average sensitivity of nodule detection is 82.62%, which indicates the effectiveness of the proposed algorithm.