Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks

Sebastian Krappe; Michaela Benz; Alexander Gryanik; Egbert Tannich; Christine Wegner; Marc Stamminger; Thomas Wittenberg; Chrisitan Münzenmayer

doi:10.1117/12.2249845

1 March 2017 Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks

Sebastian Krappe, Michaela Benz, Alexander Gryanik, Egbert Tannich, Christine Wegner, Marc Stamminger, Thomas Wittenberg, Chrisitan Münzenmayer

Author Affiliations +

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400B (2017) https://doi.org/10.1117/12.2249845
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Malaria is one of the world’s most common and serious tropical diseases, caused by parasites of the genus plasmodia that are transmitted by Anopheles mosquitoes. Various parts of Asia and Latin America are affected but highest malaria incidence is found in Sub-Saharan Africa. Standard diagnosis of malaria comprises microscopic detection of parasites in stained thick and thin blood films. As the process of slide reading under the microscope is an error-prone and tedious issue we are developing computer-assisted microscopy systems to support detection and diagnosis of malaria. In this paper we focus on a deep learning (DL) approach for the detection of plasmodia and the evaluation of the proposed approach in comparison with two reference approaches. The proposed classification schemes have been evaluated with more than 180,000 automatically detected and manually classified plasmodia candidate objects from so-called thick smears. Automated solutions for the morphological analysis of malaria blood films could apply such a classifier to detect plasmodia in the highly complex image data of thick smears and thereby shortening the examination time. With such a system diagnosis of malaria infections should become a less tedious, more reliable and reproducible and thus a more objective process. Better quality assurance, improved documentation and global data availability are additional benefits.

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Sebastian Krappe, Michaela Benz, Alexander Gryanik, Egbert Tannich, Christine Wegner, Marc Stamminger, Thomas Wittenberg, and Chrisitan Münzenmayer "Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400B (1 March 2017); https://doi.org/10.1117/12.2249845

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