Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears

Journal article

Objective: This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones.
Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear... Expand abstract

Authors: Yang, F; Poostchi, M; Yu, H; Zhou, Z; Silamut, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Journal: IEEE Journal of Biomedical and Health Informatics
• Volume: 24
• Issue: 5
• Pages: 1427-1438
• Publication date: 2019-09-23
• Acceptance date: 2019-08-25
• DOI: 10.1109/jbhi.2019.2939121
• EISSN: 2168-2208
• ISSN: 2168-2194
• Pmid: 31545747
• Language: English
• Keywords: Blood; convolutional neural networks; FFR; Deep learning; malaria; Image segmentation; computer-aided diagnosis; Sensitivity; Diseases; Support vector machines; Feature extraction