# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 4 Discussion  
The results reported in the previous section show the ability of the Inception-based classifiers to deal with the problem at hand. Both Inception-V1 and Inception-V3 architectures achieved good performance. Moreover, the soft-voting ensemble strategy made it possible to obtain an improvement of the overall performance.  
The best performance has been achieved by using the soft-voting strategy with an ensemble of Inception-V1 models and a CT depth of 35 slices. As for this configuration, the ensemble classifier model operating on the three-way classification task discussed above achieved an accuracy of 97.06%, which was an improvement of about 1.40% with respect to the performance of the related monolithic model. Similarly, for the NCP-vs-rest task, the ensemble model achieved a sensitivity of 97.17%, showing an increase of about 1% with respect to the monolithic model.  
The results also highlighted a slight improvement of the overall performance compared with a small increase of the scan depth. Experiments have been performed on two CT scan depths according to the memory constraint of the GPU device. As experiments have been carried out on a NVIDIA V100 equipped with 16 GB of memory, it is plausible to assume that further performance improvement might be obtained increasing the CT scan depth with devices equipped with more memory.  
A comparison with other related works found in the literature show the effectiveness of the proposed approach. Taking into account the study presented in [32], we did not present results on different datasets. This study was aimed at assessing the generalization ability of the models designed to detect COVID-19 infections. Experiments show that all analyzed models have a poor generalization ability. We observed a performance close to random guessing when evaluating the model on a dataset never seen before. In the study, it is assumed that the negative performance is related to various factors, such as the patient demographics, pre-existing clinical conditions, and differences in image acquisition or reconstruction.  
Further issues related to these datasets are also due to the fact that they have been released in a non-standardized way. In fact, the majority of these datasets have been pre-processed according to specific needs and then released using a not standard format for these medical images. Often, the way these CT scans were processed is not clearly declared. Therefore, it is very difficult or impossible to process these datasets to meet specific constraints required by a given classifier model. This problem could be resolved releasing the datasets using a standard format for these type of images such as the DICOM (Digital Imaging and COmmunications in Medicine) format. In fact, DICOM files can be easily processed to visualize the images according specific parameters such as the window width and the window center, allowing uniform CT scans according to identical parameters. Moreover, a DICOM file does not contain only the image data. It also contains a header that stores additional information related to the patient and to the CT scan. This information could be used to filter out instances deemed not suitable for the test in hand.