Open AccessUnsupervised Convolutional Filter Learning for COVID-19 Classification
Author(s) -
Sakthi Ganesh Mahalingam,
Saichandra Pandraju
Publication year - 2021
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.350509
Subject(s) - autoencoder , covid-19 , computer science , artificial intelligence , filter (signal processing) , convolutional neural network , unsupervised learning , identification (biology) , machine learning , deep learning , pattern recognition (psychology) , medicine , pathology , computer vision , biology , botany , disease , infectious disease (medical specialty)
The outbreak of the SARS CoV-2, referred to as COVID-19, was initially reported in 2019 and has swiftly spread around the world. The identification of COVID-19 cases is one of the key factors to inhibit the spread of the virus. While there are multiple ways to diagnose COVID-19, these techniques are often expensive, time-consuming, or not readily available. Detection of COVID-19 using a radiological examination of Chest X-Rays provides a more viable, rapid, and efficient solution as it is easily available in most countries. The paper outlines a method that employs an unsupervised convolutional filter learning using Convolutional Autoencoder (CAE) followed by applying it to COVID-19 classification as a downstream task. This shows that the proposed technique provides state-of-the-art results with an average accuracy of 99.7%, AUC of 99.7%, specificity of 99.8%, sensitivity of 99.6%, and F1-score of 99.6%. We release the data and code for this work to aid further research.