COVID-CT-MD: COVID-19 Computed Tomography Scan Dataset Applicable in Machine Learning and Deep Learning

COVID-19, CAP and Normal subjects are placed in separate folders, within which patients are arranged in folders, followed by CT scan slices in DICOM format. Index.csv is related to the patients having slice-level and lobe-level labels. The indices given to patients in Index.csv file are then used in Slice-level-labels.npy and Lobe-level-labels.npy to indicate the slice and lobe labels. Slice-level-labels.npy is a 2D binary Numpy array in which the existence of infection in a specific slice is indicated by 1 and the lack of infection is shown by 0. In Slice-level-labels.npy, the first dimension represents the case index and the second one represents the slice numbers. Lobe-level-labels.npy is a 3D binary Numpy array in which the existence of infection in a specific lobe and slice is determined by 1 in the corresponding element of the array. Like the slice-level array, in Lobe-level-labels.npy, the two first dimensions represent the case index and slice numbers respectively. The third dimension shows the lobe indices which are specified as follows: 0 : Left Lower Lobe (LLL) 1 : Left Upper Lobe (LUL) 2 : Right Lower Lobe (RLL) 3 : Right Middle Lobe (RML) 4 : Right Upper Lobe (RUL) It is worth noting that CT slices are sorted based on the "Slice Location" value stored in the corresponding DICOM tag "(0020,1041) - DS - Slice Location". The slice-level and lobe-level labels are provided according to described slice order. The researchers, however, can re-arrange the slices using other CT attributes based on their preference, as long as they re-arrange the labels accordingly. The COVID-CT-MD dataset is also accompanied with the clinical data, stored in "Clinical-data.csv". Finally, to facilitate the inter-observer reliability studies, labels assigned by the three radiologists are separately provided in "Radiogists-seperated-labels.csv".