COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network
Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnost...
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MDPI AG
2022-02-01
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Online Access: | https://www.mdpi.com/2227-9032/10/3/422 |
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author | Happy Nkanta Monday Jianping Li Grace Ugochi Nneji Saifun Nahar Md Altab Hossin Jehoiada Jackson |
author_facet | Happy Nkanta Monday Jianping Li Grace Ugochi Nneji Saifun Nahar Md Altab Hossin Jehoiada Jackson |
author_sort | Happy Nkanta Monday |
collection | DOAJ |
description | Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases. |
first_indexed | 2024-03-09T19:46:57Z |
format | Article |
id | doaj.art-0ceb7a4f503b4b4b90f3ef7ff686af52 |
institution | Directory Open Access Journal |
issn | 2227-9032 |
language | English |
last_indexed | 2024-03-09T19:46:57Z |
publishDate | 2022-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Healthcare |
spelling | doaj.art-0ceb7a4f503b4b4b90f3ef7ff686af522023-11-24T01:21:17ZengMDPI AGHealthcare2227-90322022-02-0110342210.3390/healthcare10030422COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule NetworkHappy Nkanta Monday0Jianping Li1Grace Ugochi Nneji2Saifun Nahar3Md Altab Hossin4Jehoiada Jackson5School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Information System and Technology, University of Missouri St. Louis, St. Louis, MO 63121, USASchool of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSince it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.https://www.mdpi.com/2227-9032/10/3/422COVID-19chest X-rayconvolutional neural networkwaveletcapsule networkpneumonia |
spellingShingle | Happy Nkanta Monday Jianping Li Grace Ugochi Nneji Saifun Nahar Md Altab Hossin Jehoiada Jackson COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network Healthcare COVID-19 chest X-ray convolutional neural network wavelet capsule network pneumonia |
title | COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network |
title_full | COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network |
title_fullStr | COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network |
title_full_unstemmed | COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network |
title_short | COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network |
title_sort | covid 19 pneumonia classification based on neurowavelet capsule network |
topic | COVID-19 chest X-ray convolutional neural network wavelet capsule network pneumonia |
url | https://www.mdpi.com/2227-9032/10/3/422 |
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