CxSE: Chest X-ray Slow Encoding CNN forCOVID-19 Diagnosis

• URL: http://arxiv.org/abs/2106.12157v1
• Date: Wed, 23 Jun 2021 04:57:39 GMT
• Title: CxSE: Chest X-ray Slow Encoding CNN forCOVID-19 Diagnosis
• Authors: Thangarajah Akilan
• Abstract summary: coronavirus needs to be detected quickly in order to quarantine positive patients so as to avoid further spread. This work proposes a new convolutional neural network architecture called'slow CNN'
• Score: 3.274290296343038
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The coronavirus continues to disrupt our everyday lives as it spreads at an exponential rate. It needs to be detected quickly in order to quarantine positive patients so as to avoid further spread. This work proposes a new convolutional neural network (CNN) architecture called 'slow Encoding CNN. The proposed model's best performance wrt Sensitivity, Positive Predictive Value (PPV) found to be SP=0.67, PP=0.98, SN=0.96, and PN=0.52 on AI AGAINST COVID19 - Screening X-ray images for COVID-19 Infections competition's test data samples. SP and PP stand for the Sensitivity and PPV of the COVID-19 positive class, while PN and SN stand for the Sensitivity and PPV of the COVID-19 negative class.

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