Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging
This work addresses the need for automated diagnostic tools in public health during the COVID-19 pandemic, but it is incremental as it applies existing deep learning methods to a new medical imaging dataset.
The paper tackles COVID-19 detection by proposing a convolutional neural network to analyze lung ultrasound images, with the trained model intended for deployment on a Raspberry Pi to predict new cases.
The new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has had a devastating effect on both everyday life, public health and the global economy. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. The need for auxiliary diagnostic tools has increased as accurate automated tool kits are not available. This paper presents a work in progress that proposes the analysis of images of lung ultrasound scans using a convolutional neural network. The trained model will be used on a Raspberry Pi to predict on new images.