{'fraction_non_alphanumeric': 0.027756360832690823, 'fraction_numerical': 0.02235929067077872, 'mean_word_length': 4.926940639269406, '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 Large Imaging Database and Novel Deep Neural Architecture for Covid-19 Diagnosis

Deep learning methodologies constitute nowadays the main approach for medical image analysis and disease prediction. Large annotated databases are necessary for developing these methodologies; such databases are difficult to obtain and to make publicly available for use by researchers and medical experts. In this paper, we focus on diagnosis of Covid-19 based on chest 3-D CT scans and develop a dual knowledge framework, including a large imaging database and a novel deep neural architecture. We introduce COV19-CT-DB, a very large database annotated for COVID-19 that consists of 7,750 3-D CT scans, 1,650 of which refer to COVID-19 cases and 6,100 to non-COVID19 cases. We use this database to train and develop the RACNet architecture. This architecture performs 3-D analysis based on a CNN-RNN network and handles input CT scans of different lengths, through the introduction of dynamic routing, feature alignment and a mask layer. We conduct a large experimental study that illustrates that the RACNet network has the best performance compared to other deep neural networks i) when trained and tested on COV19-CT-DB; ii) when tested, or when applied, through transfer learning, to other public databases.