DEEP NEURAL NETWORK CLASSIFICATION MODELS FOR COVID-19 DETECTION IN X-RAY IMAGES: TOWARDS FEW-SHOT AND META PSEUDO LABELS LEARNING
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Date
2022-05
Authors
Abdukarimov, Yerkin
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Since the beginning of COVID-19 pandemic, efficient methods to detect the infection
is in urgent need around the globe. There are existing testing systems created to
handle the spread of infection. However, considering the fact that those systems can
require substantial amount of time, and moreover, are not always commonly available,
alternative testing methods have become necessary. In this study, our proposal is to
use Convolutional Neural Networks (CNN) to detect COVID-19 infection appearance
on chest X-ray images. During this study, we have utilized 9 different datasets
that have been used to train and evaluate 5 CNN algorithms. The datasets contain
X-ray images labeled as COVID-19, Pneumonia or Normal. CNN algorithms that
have been used to classify the data include ResNet-50, VGG-16, AlexNet, Inception-
V3, InceptionResNet-V2. Empirically, we have established that InceptionResNet-V2
model provides best evaluation accuracy averagely reaching accuracy of 95.1% which
is a very promising result considering the medical nature of the domain used. To train
models with a limited amount of data, we decided to use a new Few-Shot Learning
method, which was able to achieve a result of 97.7%. We have also used a semisupervised
learning method Meta Pseudo Labels, which allowed us to train models
with a poor labeled datasets. The approach has also demonstrated promising results
achieving 92.5% of accuracy on the data labeled only for 16%. Our performance results
have made it possible to produce a heat map of an X-ray image that illustrates
lung areas that are most influential for a model to distinguish COVID-19 images from
images labeled as Normal. Additionally, we have provided 2-dimensional T-SNE representation
that illustrates how the CNN models observe the data in lower dimension
and separate it into clusters.
Description
Keywords
CNN, COVID-19, pandemics, Type of access: Gated Access, Research Subject Categories::TECHNOLOGY, Convolutional Neural Networks
Citation
Abdukarimov, Y. (2022). Deep Neural Network Classification Models for COVID-19 Detection in X-ray Images: towards Few-Shot and Meta Pseudo Labels Learning (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan