Abstract
Identifying patients, infected with the virulent disease, malaria requires a reliable and quick diagnosis of blood cells. This paper presents a computer-aided diagnosing (CADx) method supported by a deep convolutional neural network (CNN) for assisting clinicians to detect malaria by medical image. We employed the VGG-19 and ResNet-50 architectures to create several models for two types of study (parasitized and uninfected erythrocytes). To enhance the model’s performance, an ensemble technique was applied, followed by which, the best model selected by performance measuring metrics. Our proposed model was qualified and examined upon a standard microscopic set of images collected from the National Institute of Health (NIH). The final result was analogized with other techniques, where the accuracy of this model was 96.7% for patient-level detection. To resolute the limitations and minimizing errors regarding automated malaria detection, the proposed model proved to be an appropriate strategy for distant regions and emergencies.
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Notes
- 1.
Data supporting the conclusions of this research are accessible at github.com/ErtezaTawsif/Malaria.
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Acknowledgements
This paper would not have been possible without the exceptional support of the Assistant Professor Khandaker Lubaba Bashar from the Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh, because her expertise has improved the research in innumerable ways and saved us from many errors; those that inevitably remain are entirely our responsibility.
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Efaz, E.T., Alam, F., Kamal, M.S. (2021). Deep CNN-Supported Ensemble CADx Architecture to Diagnose Malaria by Medical Image. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_20
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