Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax
Abstract Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neu...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-87979-5 |
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| author | Diego A. Ramos-Briceño Alessandro Flammia-D’Aleo Gerardo Fernández-López Fhabián S. Carrión-Nessi David A. Forero-Peña |
| author_facet | Diego A. Ramos-Briceño Alessandro Flammia-D’Aleo Gerardo Fernández-López Fhabián S. Carrión-Nessi David A. Forero-Peña |
| author_sort | Diego A. Ramos-Briceño |
| collection | DOAJ |
| description | Abstract Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images. Previous models efficiently detected malaria parasites in red blood cells but had difficulty differentiating between species. We propose a CNN-based model for classifying cells infected by P. falciparum, P. vivax, and uninfected white blood cells from thick blood smears. Our best-performing model utilizes a seven-channel input and correctly predicted 12,876 out of 12,954 cases. We also generated a cross-validation confusion matrix that showed the results of five iterations, achieving 63,654 out of 64,126 true predictions. The model’s accuracy reached 99.51%, a precision of 99.26%, a recall of 99.26%, a specificity of 99.63%, an F1 score of 99.26%, and a loss of 2.3%. We are now developing a system based on real-world quality images to create a comprehensive detection tool for remote regions where trained microscopists are unavailable. |
| format | Article |
| id | doaj-art-a0ffbcaa59ab46e9801d435f8ad5d634 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a0ffbcaa59ab46e9801d435f8ad5d6342025-02-02T12:17:28ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-87979-5Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivaxDiego A. Ramos-Briceño0Alessandro Flammia-D’Aleo1Gerardo Fernández-López2Fhabián S. Carrión-Nessi3David A. Forero-Peña4School of Systems Engineering, Faculty of Engineering, Universidad Metropolitana de CaracasSchool of Systems Engineering, Faculty of Engineering, Universidad Metropolitana de CaracasDepartment of Electronics and Circuits, Faculty of Engineering, Universidad Simón BolívarBiomedical Research and Therapeutic Vaccines InstituteBiomedical Research and Therapeutic Vaccines InstituteAbstract Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images. Previous models efficiently detected malaria parasites in red blood cells but had difficulty differentiating between species. We propose a CNN-based model for classifying cells infected by P. falciparum, P. vivax, and uninfected white blood cells from thick blood smears. Our best-performing model utilizes a seven-channel input and correctly predicted 12,876 out of 12,954 cases. We also generated a cross-validation confusion matrix that showed the results of five iterations, achieving 63,654 out of 64,126 true predictions. The model’s accuracy reached 99.51%, a precision of 99.26%, a recall of 99.26%, a specificity of 99.63%, an F1 score of 99.26%, and a loss of 2.3%. We are now developing a system based on real-world quality images to create a comprehensive detection tool for remote regions where trained microscopists are unavailable.https://doi.org/10.1038/s41598-025-87979-5MalariaPlasmodium infectionArtificial intelligenceDeep learningNeural network modelMedical image processing |
| spellingShingle | Diego A. Ramos-Briceño Alessandro Flammia-D’Aleo Gerardo Fernández-López Fhabián S. Carrión-Nessi David A. Forero-Peña Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax Scientific Reports Malaria Plasmodium infection Artificial intelligence Deep learning Neural network model Medical image processing |
| title | Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax |
| title_full | Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax |
| title_fullStr | Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax |
| title_full_unstemmed | Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax |
| title_short | Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax |
| title_sort | deep learning based malaria parasite detection convolutional neural networks model for accurate species identification of plasmodium falciparum and plasmodium vivax |
| topic | Malaria Plasmodium infection Artificial intelligence Deep learning Neural network model Medical image processing |
| url | https://doi.org/10.1038/s41598-025-87979-5 |
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