An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis
Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/8/3/66 |
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| author | Andrea Loddo Corrado Fadda Cecilia Di Ruberto |
| author_facet | Andrea Loddo Corrado Fadda Cecilia Di Ruberto |
| author_sort | Andrea Loddo |
| collection | DOAJ |
| description | Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the <i>P. falciparum</i> stages of life and (iii) evaluating the robustness of the models with cross-dataset experiments. Eleven well-known convolutional neural networks on two public datasets have been exploited. The results show that the networks have great accuracy in binary classification, even though they lack few samples per class. Moreover, the cross-dataset experiments exhibit the need for some further regulations. In particular, ResNet-18 achieved up to 97.68% accuracy in the binary classification, while DenseNet-201 reached 99.40% accuracy on the multiclass classification. The cross-dataset experiments exhibit the limitations of deep learning approaches in such a scenario, even though combining the two datasets permitted DenseNet-201 to reach 97.45% accuracy. Naturally, this needs further investigation to improve the robustness. In general, DenseNet-201 seems to offer the most stable and robust performance, offering as a crucial candidate to further developments and modifications. Moreover, the mobile-oriented architectures showed promising and satisfactory performance in the classification of malaria parasites. The obtained results enable extensive improvements, specifically oriented to the application of object detectors for type and stage of life recognition, even in mobile environments. |
| first_indexed | 2024-03-09T19:35:54Z |
| format | Article |
| id | doaj.art-c666ba8208da49e08c28f8770aa5b452 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T19:35:54Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-c666ba8208da49e08c28f8770aa5b4522023-11-24T01:54:59ZengMDPI AGJournal of Imaging2313-433X2022-03-01836610.3390/jimaging8030066An Empirical Evaluation of Convolutional Networks for Malaria DiagnosisAndrea Loddo0Corrado Fadda1Cecilia Di Ruberto2Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, ItalyMalaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the <i>P. falciparum</i> stages of life and (iii) evaluating the robustness of the models with cross-dataset experiments. Eleven well-known convolutional neural networks on two public datasets have been exploited. The results show that the networks have great accuracy in binary classification, even though they lack few samples per class. Moreover, the cross-dataset experiments exhibit the need for some further regulations. In particular, ResNet-18 achieved up to 97.68% accuracy in the binary classification, while DenseNet-201 reached 99.40% accuracy on the multiclass classification. The cross-dataset experiments exhibit the limitations of deep learning approaches in such a scenario, even though combining the two datasets permitted DenseNet-201 to reach 97.45% accuracy. Naturally, this needs further investigation to improve the robustness. In general, DenseNet-201 seems to offer the most stable and robust performance, offering as a crucial candidate to further developments and modifications. Moreover, the mobile-oriented architectures showed promising and satisfactory performance in the classification of malaria parasites. The obtained results enable extensive improvements, specifically oriented to the application of object detectors for type and stage of life recognition, even in mobile environments.https://www.mdpi.com/2313-433X/8/3/66computer visiondeep learningimage processingmalaria parasites detectionmalaria parasites classification |
| spellingShingle | Andrea Loddo Corrado Fadda Cecilia Di Ruberto An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis Journal of Imaging computer vision deep learning image processing malaria parasites detection malaria parasites classification |
| title | An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis |
| title_full | An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis |
| title_fullStr | An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis |
| title_full_unstemmed | An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis |
| title_short | An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis |
| title_sort | empirical evaluation of convolutional networks for malaria diagnosis |
| topic | computer vision deep learning image processing malaria parasites detection malaria parasites classification |
| url | https://www.mdpi.com/2313-433X/8/3/66 |
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