COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/..
Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
|---|---|
| Enthalten in: |
IEEE access : practical innovations, open solutions - 8(2020) vom: 20., Seite 149808-149824 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Horry, Michael J [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
CNN models |
|---|
| Anmerkungen: |
Date Revised 04.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1109/ACCESS.2020.3016780 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM334671744 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM334671744 | ||
| 003 | DE-627 | ||
| 005 | 20240404233721.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1109/ACCESS.2020.3016780 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1364.xml |
| 035 | |a (DE-627)NLM334671744 | ||
| 035 | |a (NLM)34931154 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Horry, Michael J |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 04.04.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. | ||
| 520 | |a Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a CNN models | |
| 650 | 4 | |a COVID-19 detection | |
| 650 | 4 | |a X-ray | |
| 650 | 4 | |a image processing | |
| 650 | 4 | |a model comparison | |
| 650 | 4 | |a ultrasound and CT based detection | |
| 700 | 1 | |a Chakraborty, Subrata |e verfasserin |4 aut | |
| 700 | 1 | |a Paul, Manoranjan |e verfasserin |4 aut | |
| 700 | 1 | |a Ulhaq, Anwaar |e verfasserin |4 aut | |
| 700 | 1 | |a Pradhan, Biswajeet |e verfasserin |4 aut | |
| 700 | 1 | |a Saha, Manas |e verfasserin |4 aut | |
| 700 | 1 | |a Shukla, Nagesh |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t IEEE access : practical innovations, open solutions |d 2013 |g 8(2020) vom: 20., Seite 149808-149824 |w (DE-627)NLM243194919 |x 2169-3536 |7 nnns |
| 773 | 1 | 8 | |g volume:8 |g year:2020 |g day:20 |g pages:149808-149824 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1109/ACCESS.2020.3016780 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 8 |j 2020 |b 20 |h 149808-149824 | ||