An Investigation on Transfer Learning for Classification of COVID-19 Chest X-Ray Images with Pre-trained Convolutional-based Architectures

Abstract

Medical image analysis techniques have been helpful for rapidly identifying COVID-19. Due to the difficulty of identifying critical visual features for many patients, machine learning techniques are promising for diagnosing infection. Given the variety of approaches to using convolutional-based architectures for medical image analysis tasks, we used the most well-known and powerful deep neural networks to provide a concise and reliable source of information on transfer learning-based methods, paving the way for future research. The article describes an investigation into the detection of COVID-19 in medical chest X-ray images using 27 pre-trained convolutional neural networks. This study utilized a binarized version of a few publicly available datasets from COVID-19 patients and healthy controls. The results of the 5-fold cross-validation demonstrate that architectures such as EfficientNet networks, MobileNet, Inception-ResNetV2, and NasNet-Large perform significantly better on the aforementioned binary pattern recognition task.

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