COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization
Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lung...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.948205/full |
| _version_ | 1817995415372431360 |
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| author | Ameer Hamza Muhammad Attique Khan Shui-Hua Wang Abdullah Alqahtani Shtwai Alsubai Adel Binbusayyis Hany S. Hussein Hany S. Hussein Thomas Markus Martinetz Hammam Alshazly |
| author_facet | Ameer Hamza Muhammad Attique Khan Shui-Hua Wang Abdullah Alqahtani Shtwai Alsubai Adel Binbusayyis Hany S. Hussein Hany S. Hussein Thomas Markus Martinetz Hammam Alshazly |
| author_sort | Ameer Hamza |
| collection | DOAJ |
| description | Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy. |
| first_indexed | 2024-04-14T02:06:42Z |
| format | Article |
| id | doaj.art-3e7723292caa4599b18d1eb5e1ce6a23 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-14T02:06:42Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-3e7723292caa4599b18d1eb5e1ce6a232022-12-22T02:18:39ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-08-011010.3389/fpubh.2022.948205948205COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimizationAmeer Hamza0Muhammad Attique Khan1Shui-Hua Wang2Abdullah Alqahtani3Shtwai Alsubai4Adel Binbusayyis5Hany S. Hussein6Hany S. Hussein7Thomas Markus Martinetz8Hammam Alshazly9Department of Computer Science, HITEC University, Taxila, PakistanDepartment of Computer Science, HITEC University, Taxila, PakistanDepartment of Mathematics, University of Leicester, Leicester, United KingdomCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, King Khalid University, Abha, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, EgyptInstitute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, GermanyFaculty of Computers and Information, South Valley University, Qena, EgyptCoronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.https://www.frontiersin.org/articles/10.3389/fpubh.2022.948205/fullcoronavirusenhancementdeep learningLSTMoptimization |
| spellingShingle | Ameer Hamza Muhammad Attique Khan Shui-Hua Wang Abdullah Alqahtani Shtwai Alsubai Adel Binbusayyis Hany S. Hussein Hany S. Hussein Thomas Markus Martinetz Hammam Alshazly COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization Frontiers in Public Health coronavirus enhancement deep learning LSTM optimization |
| title | COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization |
| title_full | COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization |
| title_fullStr | COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization |
| title_full_unstemmed | COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization |
| title_short | COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization |
| title_sort | covid 19 classification using chest x ray images a framework of cnn lstm and improved max value moth flame optimization |
| topic | coronavirus enhancement deep learning LSTM optimization |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.948205/full |
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