ABSTRACT
In order to establish the correct protocol for COVID-19 treatment, estimating the percentage of COVID-19 specific infection within the lung tissue can be an important tool. This article describes the approach we used in order to estimate the COVID-19 infection percentage on lung CT scan slices within the Covid-19-Infection-Percentage-Estimation-Challenge. Our method frames the regression problem as a multi-tasking process and is based on modern training pipelines and architectures that correspond to state of the art models on image classification tasks. It obtained the best score on the validation dataset and ranked third in the testing phase within the competition. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
We train a deep learning algorithm to flag potential covid-19 infected in chest x-rays. The deep learning algorithm used is a Convolutional Neural Network that is 121 layers deep. Due to the lack of a large open-source of covid-19 infected x-ray images, we combine data from five different sources. Combined, the dataset has 17,194 images that are used for training procedure. The model classifies a given chest X-ray image as either a "Normal", "Covid-19", or a 'Pneumonia"infection. The trained model has a 0.93 F1 Score and 93.496% accuracy. © 2022 IEEE.