Early-stage COVID-19 diagnosis in presence of limited posteroanterior chest X-ray images via novel Pinball-OCSVM

10/16/2020
by   Sanjay Kumar Sonbhadra, et al.
0

It is evident that the infection with this severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) starts with the upper respiratory tract and as the virus grows, the infection can progress to lungs and develop pneumonia. According to the statistics, approximately 14% of the infected people with COVID-19 have severe cough and shortness of breath due to pneumonia, because as the viral infection increases, it damages the alveoli (small air sacs) and surrounding tissues. The conventional way of COVID-19 diagnosis is reverse transcription polymerase chain reaction (RT-PCR), which is less sensitive during early stages specially, if the patient is asymptomatic that may further lead to more severe pneumonia. To overcome this problem an early diagnosis method is proposed in this paper via one-class classification approach using a novel pinball loss function based one-class support vector machine (PB-OCSVM) considering posteroanterior chest X-ray images. Recently, several automated COVID-19 diagnosis models have been proposed based on various deep learning architectures to identify pulmonary infections using publicly available chest X-ray (CXR) where the presence of less number of COVID-19 positive samples compared to other classes (normal, pneumonia and Tuberculosis) raises the challenge for unbiased learning in deep learning models that has been solved using class balancing techniques which however should be avoided in any medical diagnosis process. Inspired by this phenomenon, this article proposes a novel PB-OCSVM model to work in presence of limited COVID-19 positive CXR samples with objectives to maximize the learning efficiency while minimize the false-positive and false-negative predictions. The proposed model outperformed over recently published deep learning approaches where accuracy, precision, specificity and sensitivity are used as performance measure parameters.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

04/23/2020

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that res...
06/07/2020

A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images

In this study, our first aim is to evaluate the ability of recent state-...
12/31/2020

New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis

Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2...
01/16/2022

Challenges in COVID-19 Chest X-Ray Classification: Problematic Data or Ineffective Approaches?

The value of quick, accurate, and confident diagnoses cannot be undermin...
12/14/2021

Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm

In recent years, people from all over the world are suffering from one o...
08/26/2020

DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs

Automated infection measurement and COVID-19 diagnosis based on Chest X-...
11/17/2020

Decision and Feature Level Fusion of Deep Features Extracted from Public COVID-19 Data-sets

The Coronavirus (COVID-19), which is an infectious pulmonary disorder, h...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.