A new ensemble learning approach to detect malaria from microscopic red blood cell images
Malaria is a life-threatening parasitic disease spread by infected female Anopheles mosquitoes. After analyzing it, microscopists detect this disease from the sample of microscopic red blood cell images. A professional microscopist is required to conduct the detection process, such an analysis may b...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-01-01
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| Series: | Sensors International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666351122000547 |
| _version_ | 1797336900821843968 |
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| author | Mosabbir Bhuiyan Md Saiful Islam |
| author_facet | Mosabbir Bhuiyan Md Saiful Islam |
| author_sort | Mosabbir Bhuiyan |
| collection | DOAJ |
| description | Malaria is a life-threatening parasitic disease spread by infected female Anopheles mosquitoes. After analyzing it, microscopists detect this disease from the sample of microscopic red blood cell images. A professional microscopist is required to conduct the detection process, such an analysis may be time-consuming and provide low-quality results for large-scale diagnoses. This paper develops an ensemble learning-based deep learning model to identify malaria parasites from red blood cell images. VGG16(Retrained), VGG19(Retrained), and DenseNet201(Retrained) are three models that are used in developing the adaptive weighted average ensemble models. To reduce the dispersion of predictions, a max voting ensemble technique is then applied in combination with adaptive weighted average ensemble models. A variety of image processing techniques are utilized including the data augmentation technique to increase the number of data and solve the overfitting problem of the model. Some other approaches of custom CNN, Transfer Learning, and CNN-Machine Learning (ML) classifier techniques are also implemented for comparing their performance with the ensemble learning model. The proposed ensemble learning model provides the best performance among all with an accuracy of 97.92% to classify parasitized and uninfected cells. Therefore, the deep learning model has the potential to diagnose malaria more accurately and automatically. |
| first_indexed | 2024-03-08T09:01:38Z |
| format | Article |
| id | doaj.art-8b24f344e6ac4b1eb1d09c5d126bbbf2 |
| institution | Directory Open Access Journal |
| issn | 2666-3511 |
| language | English |
| last_indexed | 2024-03-08T09:01:38Z |
| publishDate | 2023-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Sensors International |
| spelling | doaj.art-8b24f344e6ac4b1eb1d09c5d126bbbf22024-02-01T06:35:15ZengKeAi Communications Co., Ltd.Sensors International2666-35112023-01-014100209A new ensemble learning approach to detect malaria from microscopic red blood cell imagesMosabbir Bhuiyan0Md Saiful Islam1Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, BangladeshDepartment of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Bangladesh; Corresponding author.Malaria is a life-threatening parasitic disease spread by infected female Anopheles mosquitoes. After analyzing it, microscopists detect this disease from the sample of microscopic red blood cell images. A professional microscopist is required to conduct the detection process, such an analysis may be time-consuming and provide low-quality results for large-scale diagnoses. This paper develops an ensemble learning-based deep learning model to identify malaria parasites from red blood cell images. VGG16(Retrained), VGG19(Retrained), and DenseNet201(Retrained) are three models that are used in developing the adaptive weighted average ensemble models. To reduce the dispersion of predictions, a max voting ensemble technique is then applied in combination with adaptive weighted average ensemble models. A variety of image processing techniques are utilized including the data augmentation technique to increase the number of data and solve the overfitting problem of the model. Some other approaches of custom CNN, Transfer Learning, and CNN-Machine Learning (ML) classifier techniques are also implemented for comparing their performance with the ensemble learning model. The proposed ensemble learning model provides the best performance among all with an accuracy of 97.92% to classify parasitized and uninfected cells. Therefore, the deep learning model has the potential to diagnose malaria more accurately and automatically.http://www.sciencedirect.com/science/article/pii/S2666351122000547Machine learningDeep learningCNNTransfer learningEnsembleMalaria |
| spellingShingle | Mosabbir Bhuiyan Md Saiful Islam A new ensemble learning approach to detect malaria from microscopic red blood cell images Sensors International Machine learning Deep learning CNN Transfer learning Ensemble Malaria |
| title | A new ensemble learning approach to detect malaria from microscopic red blood cell images |
| title_full | A new ensemble learning approach to detect malaria from microscopic red blood cell images |
| title_fullStr | A new ensemble learning approach to detect malaria from microscopic red blood cell images |
| title_full_unstemmed | A new ensemble learning approach to detect malaria from microscopic red blood cell images |
| title_short | A new ensemble learning approach to detect malaria from microscopic red blood cell images |
| title_sort | new ensemble learning approach to detect malaria from microscopic red blood cell images |
| topic | Machine learning Deep learning CNN Transfer learning Ensemble Malaria |
| url | http://www.sciencedirect.com/science/article/pii/S2666351122000547 |
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