1. CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images
- Author
-
Sousa, Azael M., Reis, Fabiano, Zerbini, Rachel, Comba, João L. D., and Falcão, Alexandre X.
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,J.3, J.6 - Abstract
Early detection of COVID-19 is vital to control its spread. Deep learning methods have been presented to detect suggestive signs of COVID-19 from chest CT images. However, due to the novelty of the disease, annotated volumetric data are scarce. Here we propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN). For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones, and the decision layer of our CNN is a support vector machine. As we have no control over the CT image acquisition, we also propose an intensity standardization approach. Our method can achieve mean accuracy and kappa values of $0.97$ and $0.93$, respectively, on a dataset with 117 CT images extracted from different sites, surpassing its counterpart in all scenarios., Comment: 4 pages. To be published in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Published
- 2021