Single-Label COVID-19 Lung Disease Classification

Abstract

Chest imaging (X-ray, CT, MRI, etc.) is often used to diagnose COVID-19 infections, and the clinical interpretation of these images may influence patient treatment plans including quarantine, ICU admission, and ventilator support. Convolutional neural networks represent state-of-the art artificial intelligence (AI) for image classification and require large and diverse training image datasets to achieve clinical grade performance. The artificial intelligence community has been focusing on building CNNs to help physicians images for COVID-19 patients, but the open community attempts have been plagued by a few main problems that are characteristic of training AI for clinical applications: Our work suggests that the incorporation of finer clinical features such as multi-class labeling of outcomes may improve clinical performance. Much more diverse and properly annotated data, as well as a proper data architecture is needed to build clinically useful AI applications. Leveraging computers and state of the art artificial intelligence could play a crucial role in helping physicians manage the spread of COVID 19, as well as provide better and more timely care for patients infected with COVID-19. The experiments described in this paper investigate whether using computer vision to detect patterns in pathological lung tissue can alleviate the pressure on healthcare systems and facilitate the management of future pandemics. Due to a relatively small dataset, we use the VGG-16 network to do transfer learning on our images. Our results show that Artificial Intelligence can be used to find pathology in patients and to determine whether there is viral or bacterial pneumonia. However, AI shows challenging results when it comes to determining specific viral or bacterial pneumonias, which is in part due to the lack of available data.