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COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images

The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription–Polymeras...

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Autores principales: Srinivas, K., Gagana Sri, R., Pravallika, K., Nishitha, K., Polamuri, Subba Rao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240113/
https://www.ncbi.nlm.nih.gov/pubmed/37362699
http://dx.doi.org/10.1007/s11042-023-15903-y
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author Srinivas, K.
Gagana Sri, R.
Pravallika, K.
Nishitha, K.
Polamuri, Subba Rao
author_facet Srinivas, K.
Gagana Sri, R.
Pravallika, K.
Nishitha, K.
Polamuri, Subba Rao
author_sort Srinivas, K.
collection PubMed
description The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription–Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called “Inception V3 with VGG16 (Visual Geometry Group)” is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.
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spelling pubmed-102401132023-06-06 COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images Srinivas, K. Gagana Sri, R. Pravallika, K. Nishitha, K. Polamuri, Subba Rao Multimed Tools Appl 1230: Sentient Multimedia Systems and Visual Intelligence The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription–Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called “Inception V3 with VGG16 (Visual Geometry Group)” is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet. Springer US 2023-06-05 /pmc/articles/PMC10240113/ /pubmed/37362699 http://dx.doi.org/10.1007/s11042-023-15903-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle 1230: Sentient Multimedia Systems and Visual Intelligence
Srinivas, K.
Gagana Sri, R.
Pravallika, K.
Nishitha, K.
Polamuri, Subba Rao
COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images
title COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images
title_full COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images
title_fullStr COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images
title_full_unstemmed COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images
title_short COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images
title_sort covid-19 prediction based on hybrid inception v3 with vgg16 using chest x-ray images
topic 1230: Sentient Multimedia Systems and Visual Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240113/
https://www.ncbi.nlm.nih.gov/pubmed/37362699
http://dx.doi.org/10.1007/s11042-023-15903-y
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