Effects of image quality and quantity on building a competitive COVID-19 diagnosis model

ABSTRACT

The outbreak of a health crisis, such as Covid-19, leads to decisions that must combine efficiency and speed. Often there is a trade-off between these two values, as the faster a decision is made, the less information is considered. This paper presents a deep learning model pipeline that balances these two values with the primary goal of classifying human lung X-rays into three categories pneumonia, covid-19 and normal. Through this process, we tried to explore whether the quality of an image can enhance the learning process to a greater extent as opposed to having larger number of images. For this purpose, we follow two approaches by viewing quality and quantity as competing objectives to increasing the level of information obtained. The first is through increasing the number of X-ray images in the dataset, and the second is through improving the quality of the X-ray images. In the first approach, our goal is achieved using a Generative Adversarial Network (GAN) to generate plasmatic covid-19 class X-rays, while in the second approach, we improve the resolution of the X-ray images. To find the hyperparameters in both approaches that lead to better system performance, we exploit the Particle Swarm Optimization (PSO) algorithm. Rapid training and hyperparameter tuning better perform through this algorithm. Our experiments depict the performance that our models, based on the two approaches, achieved. Accuracy reaches 93% while sensitivity reaches 90% over Covid-19 cases. Finally, we conclude which characteristic, quality or quantity, is most useful in our case. © 2021 IEEE.