Kvak, D., Bendik, M., & Chromcova, A. (2022). Towards clinical practice: Design and implementation of convolutional neural network-based assistive diagnosis system for covid-19 case detection from chest x-ray images. arXiv preprint arXiv:2203.10596.
Abstract:
One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.
