A light CNN for detecting COVID-19 from CT scans of the chest
Abstract
Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.03% with an improvement of about 3.2% in the first dataset arrangement and of about 2.1% in the second dataset arrangement. The obtained gain, though of low entity, can be really important in medical diagnosis and, in particular, for Covid-19 scenario. Also the average classification time on a high-end workstation, 1.25 s, is very competitive with respect to that of more complex CNN designs, 13.41 s, witch require pre-processing. The proposed CNN can be executed on medium-end laptop without GPU acceleration in 7.81 s: this is impossible for methods requiring GPU acceleration. The performance of the method can be further improved with efficient pre-processing strategies for witch GPU acceleration is not necessary.
- Publication:
-
Pattern Recognition Letters
- Pub Date:
- December 2020
- DOI:
- 10.1016/j.patrec.2020.10.001
- arXiv:
- arXiv:2004.12837
- Bibcode:
- 2020PaReL.140...95P
- Keywords:
-
- Deep Learning;
- CNN;
- Pattern Recognition;
- COVID-19;
- Electrical Engineering and Systems Science - Image and Video Processing;
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning
- E-Print:
- Pattern Recognition Letters. 140 (2020) 95-100