A Comparative Study of Deep Learning Networks for COVID-19 Recognition in Chest X-ray Images

Abstract

The COVID-19 pandemic is devastatingly affecting the health and well-being of the worldwide population. A basic advance in the battle against it resides in effective screening of infected patients, with one of the key screening approaches such as radiological imaging based on chest radiography. Faced with this challenge, various artificial intelligence (AI) frameworks, mostly based on deep learning, have been proposed and results have been getting better and very promising as the precision of positive cases recognition is constantly refined. In the light of previous work on automated X-ray image screening, we train several deep convolutional networks for the classification of chest pathologies into : normal, pneumonia, and COVID-19. We use three open-source and one private dataset for the validation of our findings. Unfortunately, data scarcity remains a big challenge hurdling COVID-19 automatic recognition research. In our case, we used a total of 518 COVID-19 positive X-ray images. We evaluate different architectures for COVID-19 recognition with different deep neural architecture.

Decoding Covid-19: Harnessing CNN Models for Chest X-Ray Classification

“…This is followed by a dropout layer that randomly removes 25% of the neurons, a convolutional layer with 64 filters, a second convolutional layer, and a layer with a maximum pooling size of 2x2. The next layers employ convolutional layers with 128 and 256 filters [9], followed by max pooling and dropout.…”