eprintid: 7313
rev_number: 8
eprint_status: archive
userid: 2
dir: disk0/00/00/73/13
datestamp: 2023-05-29 23:30:05
lastmod: 2023-05-29 23:30:07
status_changed: 2023-05-29 23:30:05
type: article
metadata_visibility: show
creators_name: Chakraborty, Gouri Shankar
creators_name: Batra, Salil
creators_name: Singh, Aman
creators_name: Muhammad, Ghulam
creators_name: Yélamos Torres, Vanessa
creators_name: Mahajan, Makul
creators_id: 
creators_id: 
creators_id: aman.singh@uneatlantico.es
creators_id: 
creators_id: vanessa.yelamos@funiber.org
creators_id: 
title: A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling
ispublished: pub
subjects: uneat_eng
divisions: uneatlantico_produccion_cientifica
divisions: uninimx_produccion_cientifica
divisions: uninipr_produccion_cientifica
full_text_status: public
keywords: deep learning; convolutional neural network; image classification; COVID-19; ensemble prediction
abstract: COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.
date: 2023
publication: Diagnostics
volume: 13
number: 10
pagerange: 1806
id_number: doi:10.3390/diagnostics13101806
refereed: TRUE
issn: 2075-4418
official_url: http://doi.org/10.3390/diagnostics13101806
access: open
language: en
citation:   Artículo Materias > Ingeniería <http://repositorio.unini.edu.mx/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html>
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros <http://repositorio.unini.edu.mx/view/divisions/uninimx=5Fproduccion=5Fcientifica.html>
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uninipr=5Fproduccion=5Fcientifica.html> Abierto Inglés COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model. metadata Chakraborty, Gouri Shankar; Batra, Salil; Singh, Aman; Muhammad, Ghulam; Yélamos Torres, Vanessa y Mahajan, Makul mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, vanessa.yelamos@funiber.org, SIN ESPECIFICAR     <http://repositorio.unini.edu.mx/id/eprint/7313/1/diagnostics-13-01806-v3.pdf>     (2023) A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.  Diagnostics, 13 (10).  p. 1806.  ISSN 2075-4418     
document_url: http://repositorio.unini.edu.mx/id/eprint/7313/1/diagnostics-13-01806-v3.pdf