The importance of standardisation – COVID-19 CT & Radiograph Image Data Stock for deep learning purpose. (December 2020) - Misztal, Krzysztof; Pocha, Agnieszka; Durak-Kozica, Martyna; Wątor, Michał; Kubica-Misztal, Aleksandra; Hartel, Marcin
Abstract: With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. In this work, we propose a new dataset which we call COVID-19 CT & Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. Moreover, it is supplemented with a carefully defined split. The aim of COVID-19 CT & Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models. Highlights: The article aims to give a public pool of CT & X-ray lungs images to increase the efficiency of detect COVID-19 disease. Binary and multiclass classifiers were trained revealing that precise labels can improve the system performance. Models trained on COVID-19 CT & X-ray Image Data Stock are more robust than models trained on other investigated databases.