An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification
Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefor...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.969268/full |
| _version_ | 1828229367149363200 |
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| author | Javeria Amin Muhammad Sharif Ghulam Ali Mallah Steven L. Fernandes |
| author_facet | Javeria Amin Muhammad Sharif Ghulam Ali Mallah Steven L. Fernandes |
| author_sort | Javeria Amin |
| collection | DOAJ |
| description | Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far. |
| first_indexed | 2024-04-12T18:33:03Z |
| format | Article |
| id | doaj.art-6887685ce80e4e65b0fd1bb8e709b17d |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T18:33:03Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-6887685ce80e4e65b0fd1bb8e709b17d2022-12-22T03:21:01ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-09-011010.3389/fpubh.2022.969268969268An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classificationJaveria Amin0Muhammad Sharif1Ghulam Ali Mallah2Steven L. Fernandes3Department of Computer Science, University of Wah, Wah Cantt, PakistanDepartment of Computer Science, COMSATS University Islamabad, Islamabad, PakistanDepartment of Computer Science, Shah Abdul Latif University, Khairpur, PakistanDepartment of Computer Science, Design and Journalism, Creighton University, Omaha, NE, United StatesMalaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.https://www.frontiersin.org/articles/10.3389/fpubh.2022.969268/fullclustersmalariaK-meanMRFOfeatures |
| spellingShingle | Javeria Amin Muhammad Sharif Ghulam Ali Mallah Steven L. Fernandes An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification Frontiers in Public Health clusters malaria K-mean MRFO features |
| title | An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification |
| title_full | An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification |
| title_fullStr | An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification |
| title_full_unstemmed | An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification |
| title_short | An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification |
| title_sort | optimized features selection approach based on manta ray foraging optimization mrfo method for parasite malaria classification |
| topic | clusters malaria K-mean MRFO features |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.969268/full |
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