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. 2023 Feb 1;13(3):534.
doi: 10.3390/diagnostics13030534.

Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease

K Hemachandran  1 , Areej Alasiry  2 , Mehrez Marzougui  2 , Shahid Mohammad Ganie  1 , Anil Audumbar Pise  3   4   5 , M Turki-Hadj Alouane  2 , Channabasava Chola  6
Affiliations

Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease

K Hemachandran et al. Diagnostics (Basel). .

Abstract

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease's impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models.

Keywords: ResNet-50; convolution neural networks; deep learning techniques; disease diagnosis; malaria; mobilenet; neural networks and RBC.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Infected cell images.
Figure 2
Figure 2
Uninfected cell images.
Figure 3
Figure 3
Block diagram of CNN model.
Figure 4
Figure 4
ReLU Algorithm.
Figure 5
Figure 5
Block diagram of MobileNetV2 model.
Figure 6
Figure 6
Block diagram of ResNet50 model.
Figure 7
Figure 7
Confusion Matrix of CNN model.
Figure 8
Figure 8
Confusion Matrix of MobileNetV2 model.
Figure 9
Figure 9
Confusion Matrix of ResNet50 model.
Figure 10
Figure 10
Accuracy and Loss of CNN model.
Figure 11
Figure 11
Accuracy and Loss of MobileNetV2 model.
Figure 12
Figure 12
Accuracy and Loss of ResNet50 model.
Figure 13
Figure 13
ROC Curve of CNN model.
Figure 14
Figure 14
ROC Curve of MobileNetV2 model.
Figure 15
Figure 15
ROC Curve of ResNet50 model.
Figure 16
Figure 16
Precision of all considered models.
Figure 17
Figure 17
Recall of all considered models.
Figure 18
Figure 18
f1-score of all considered models.
See this image and copyright information in PMC

References

    1. World malaria report 2022. [(accessed on 7 January 2023)]. Available online: https://www.who.int/teams/global-malaria-programme/reports/world-malaria....
    1. Wongsrichanalai C., Barcus M.J., Muth S., Sutamihardja A., Wernsdorfer W.H. Defining and Defeating the Intolerable Burden of Malaria III: Progress and Perspectives. American Society of Tropical Medicine and Hygiene; Arlington, VA, USA: 2007. A Review of Malaria Diagnostic Tools: Microscopy and Rapid Diagnostic Test (RDT) - PubMed
    1. Schochetman G., Ou C.Y., Jones W.K. Polymerase chain reaction. J. Infect. Dis. 1988;158:1154–1157. doi: 10.1093/infdis/158.6.1154. - DOI - PubMed
    1. Azikiwe C.C., Ifezulike C.C., Siminialayi I.M., Amazu L.U., Enye J.C., Nwakwunite O.E. A comparative laboratory diagnosis of malaria: Microscopy versus rapid diagnostic test kits. Asian Pac. J. Trop Biomed. 2021;2:307–310. doi: 10.1016/S2221-1691(12)60029-X. - DOI - PMC - PubMed
    1. Mirahmadi H., Rahmati-Balaghaleh M., Afzalaghaee M., .Zarean M., Shamsian S.A., Mehravaran A., Raissi V., Etemadi S. Detection of malaria using blood smear by light microscopy, RDT and nested-PCR for suspected patients in south-eastern Iran. Gene Re. 2021;25:101339. doi: 10.1016/j.genrep.2021.101339. - DOI

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