| Full text | |
| Author(s): |
Sousa, Azael M.
;
Reis, Fabiano
;
Zerbini, Rachel
;
Comba, Joao L. D.
;
Falcao, Alexandre X.
;
IEEE
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC); v. N/A, p. 4-pg., 2021-01-01. |
| 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. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/03940-5 - Interactive Learning of Visual Dictionaries Applied to Image Classification |
| Grantee: | César Christian Castelo Fernández |
| Support Opportunities: | Scholarships in Brazil - Doctorate |