Few-Shot Image Classification for Automatic COVID-19 Diagnosis

Developing robust and performant methods for diagnosing COVID-19, particularly for triaging processes, is crucial. This study introduces a completely automated system to detect COVID-19 by means of the analysis of Chest X-Ray scans (CXR). The proposed methodology is based on few-shot techniques, enabling to work on small image datasets. Moreover, a set of additions have been done to enhance the diagnostic capabilities. First, a network to extract the lung region to rely only on the most relevant image area. Second, a new cost function to penalize each misclassification according to the clinical consequences. Third, a system to combine different predictions from the same image to increase the robustness of the diagnoses. The proposed approach was validated on the public dataset COVIDGR-1.0, yielding a classification accuracy of 79.10% ± 3.41% and, thus, outperforming other state-of-the-art methods. In conclusion, the proposed methodology has proven to be suitable for the diagnosis of COVID-19.

keywords: chest X-Ray, COVID-19, deep neural networks, few-shot classification