Talk · International Conference on Electronics, Computer and Computation · November 25, 2021

Few-Shot Learning Approach for COVID-19 Detection from X-Ray Images

R. R. Abdrakhmanov, Meirzhan Altynbekov, Assanali Abu, Adai Shomanov, Dmitriy Viderman, Min-Ho Lee

Abstract

The end of 2019 and the beginning of 2020 were accompanied by an exponential spread of COVID-19 infection (a viral disease). This later led to a pandemic situation all over the planet. Such a rapid infection of people with the virus (SARS-CoV-2) from each other was caused by the fact that the symptoms of this disease are very similar to ordinary ARVI (acute respiratory viral infection). This in turn complicates the identification of a patient with a new virus. In order to isolate and contain the further spread of the virus, effective and rapid methods are needed to identify patients at an early stage. In our research work, we propose to use the few-shot method. This method is effective with a small amount of input data, training with few-shot is aimed at creating accurate machine learning models with less training data. Since the size of the input data is a factor determining the cost of resources (such as time costs), it is possible to reduce the cost of data analysis by using few-shot learning. The obtained results include the highest accuracy of 97.7% for 10 shots of COVID-19 X-ray images, which implies the effectiveness of the proposed approach. Notably, it was discovered that the accuracy of the approach directly correlates with the number of COVID-19 samples used for training.

COVID-19 diagnosis using AIDomain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and EducationCOVID-19 detectionfew-shot learningchest X-ray images

Few-Shot Learning Approach for COVID-19 Detection from X-Ray Images

Abstract

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