Differential Privacy Practice on Diagnosis of COVID-19 Radiology Imaging Using EfficientNet

Abstract

Medical sciences are an important application area of artificial intelligence. Healthcare requires meticulousness in the whole process from collecting data to processing. It should also be handled in terms of data quality, data size, and data privacy. Various data are used within the scope of the COVID-19 outbreak struggle. Medical and location data collected from mobile phones and wearable devices are used to prevent the spread of the epidemic. In addition to this, artificial intelligence approaches are presented by using medical images in order to identify COVID-19 infected people. However, studies should be carried out by taking care not to endanger the security of the data, people, and countries needed for these useful applications. Therefore, differential privacy (DP) application, which was an interesting research subject, has been included in this study. CXR images have been collected from COVID-19 infected 139 and a total of 373 public data sources were used for a diagnostic concept. It has been trained with EfficientNet- B0, a recent and robust deep learning model, and proposal the possibility of infected with an accuracy of 94.7%. Other evaluation parameters were also discussed in detail. Despite the data constraint, this performance showed that it can be improved by augmenting the dataset. The most important aspect of the study was the proposal of differential privacy practice for such applications to be reliable in real-life use cases. With this view, experiments were repeated with DP applied images and the results obtained were presented. Here, Private Aggregation of Teacher Ensembles (PATE) approach was used to ensure privacy assurance.

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