MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis
This addresses the problem of limited data for AI-based COVID-19 diagnosis from CT scans for medical researchers, but it is incremental as it primarily provides a new dataset with a standard model.
The authors tackled the lack of publicly available annotated COVID-19 CT datasets by introducing COV19-CT-DB, a database of about 5,000 3-D CT scans, and developed a CNN-RNN deep learning model for COVID-19 detection, reporting its performance on this new dataset.
Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for chest CT scan analysis and disease prediction. However, large annotated databases are necessary for developing deep learning models that are able to provide COVID-19 diagnosis across various medical environments in different countries. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-enabled diagnosis methods of COVID-19 based on CT scans. In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5,000 3-D CT scans, We have split the database in training, validation and test datasets. The former two datasets can be used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. We also present a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database.