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

Krappe S, Benz M, Gryanik A, Tannich E, Wegner C, Stamminger M, Wittenberg T, Münzenmayer C (2017)

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2017

Journal

Proceedings of SPIE Society of Photo-optical Instrumentation Engineers (SPIE)

Publisher: SPIE-INT SOC OPTICAL ENGINEERING, PO BOX 10, BELLINGHAM, WA 98227-0010 USA

Book Volume: 10140

DOI: 10.1117/12.2249845

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 socalled 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.

Authors with CRIS profile

Sebastian Krappe Chair of Visual Computing (LGDV) Marc Stamminger Chair of Visual Computing (LGDV) Thomas Wittenberg Technische Fakultät

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Krappe, S., Benz, M., Gryanik, A., Tannich, E., Wegner, C., Stamminger, M.,... Münzenmayer, C. (2017). Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks. In Proceedings of the Medical Imaging: Digital Pathology 2017. SPIE-INT SOC OPTICAL ENGINEERING, PO BOX 10, BELLINGHAM, WA 98227-0010 USA.

MLA:

Krappe, Sebastian, et al. "Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks." Proceedings of the Medical Imaging: Digital Pathology 2017 SPIE-INT SOC OPTICAL ENGINEERING, PO BOX 10, BELLINGHAM, WA 98227-0010 USA, 2017.

BibTeX: Download