CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images

• URL: http://arxiv.org/abs/2111.08710v1
• Date: Tue, 16 Nov 2021 15:03:42 GMT
• Title: CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images
• Authors: Azael M. Sousa, Fabiano Reis, Rachel Zerbini, Jo\~ao L. D. Comba and Alexandre X. Falc\~ao
• Abstract summary: 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.
• Score: 58.720142291102135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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