A novel framework based on deep learning for COVID-19 diagnosis from X-ray images
©2023 JavadiMoghaddam..
Background: The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease.
Methods: This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks.
Results: Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81%.
Conclusion: This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
PeerJ. Computer science - 9(2023) vom: 22., Seite e1375 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
JavadiMoghaddam, SeyyedMohammad [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 detection |
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| Anmerkungen: |
Date Revised 01.07.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.7717/peerj-cs.1375 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM358480353 |
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| 100 | 1 | |a JavadiMoghaddam, SeyyedMohammad |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A novel framework based on deep learning for COVID-19 diagnosis from X-ray images |
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| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a ©2023 JavadiMoghaddam. | ||
| 520 | |a Background: The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease | ||
| 520 | |a Methods: This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks | ||
| 520 | |a Results: Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81% | ||
| 520 | |a Conclusion: This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 detection | |
| 650 | 4 | |a Deep learning model | |
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| 650 | 4 | |a X-ray images | |
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