# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 2 Materials and Methods
### Dataset Used for Experiments  
The study is based on the China Consortium of Chest CT Image Investigation (CCCII) dataset [14]. CC-CCII is a large dataset built collecting chest CT scans from six hospitals in China. It consists of 408,350 slices obtained from 4167 scans. The dataset includes chest CT scans from patients affected by both novel corona virus pneumonia (NCP) and common pneumonia (CP) as well as from normal controls (Normal) (see Table 1). As reported in the table, CC-CCII is an imbalanced dataset. NCP and CP instances are equally represented, whereas Normal instances represent the minority class. As for the CP group, it consists of CT scans from patients affected by viral, bacterial, and mycoplasma pneumonia. Chest CT scans were obtained using a window width of 1200 and window center of \(-600\), and the slices of size \(512\times 512\) were converted from Hounsfield Units (HU) values to grayscale. The dataset is released as a set of ordered PNG images representing the slices of each CT scan.  
To the best of our knowledge, CC-CCII is the largest publicly available dataset and the only one classifying the scans according to the before described classes. However, CC-CCII comes with some issues. As highlighted in [26], CC-CCII contains damaged data, non-unified data type, repeated and noisy slices, disordered slices, and non-segmented slices. In the same work, the authors addressed these issues releasing CLEAN-CC-CCII, a cleaned version of the CC-CCII dataset, consisting of 340,190 slices from 4023 scans (see Table 2).  
In particular, in CLEAN-CC-CCII:  
* Damaged data have been removed. This consists of around 10% of the CC-CCII downloadable zip files that cannot be successfully decompressed;
* All slice image files have been unified to the PNG format without losing the information of the original files;
* Duplicated slices have been manually removed to avoid redundant data by scanning all the images in CC-CCII;
* All scans of CC-CCII have been analyzed to rearrange the disordered slices to the correct order;
* An open-source K-Means-based method has been used to segment lungs from the CT slices and remove the white background.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **\# CT Scans** & **\# Slices** \\ \hline Normal & 1078 & 95,756 \\ CP & 1545 & 156,523 \\ NCP & 1544 & 156,071 \\ Total & 4167 & 408,350 \\ \hline \end{tabular}
\end{table}
Table 1: CC-CCII dataset. This table reports the number of chest CT scans and slices for patients without lung lesions (i.e., Normal), with common pneumonia (i.e., CP) and with novel coronavirus pneumonia (i.e., NCP). The last row reports the overall size of the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **\# CT Scans** & **\# Slices** \\ \hline Normal & 966 & 73,635 \\ CP & 1523 & 135,038 \\ NCP & 1534 & 131,517 \\ Total & 4023 & 340,190 \\ \hline \end{tabular}
\end{table}
Table 2: CLEAN-CC-CCII dataset. This table reports the number of chest CT scans and slices for the CLEAN-CC-CCII dataset. The last row reports the overall size of the dataset.  
As the issues identified in CC-CCII may impact the training phase of a model, in this work, the cleaned dataset CLEAN-CC-CCII has been used.