Image analysis and machine learning for detecting malaria
By Mahdieh Poostchi, Kamolrat Silamut, Richard James Maude, Stefan Jaeger, George R. Thoma. Translational Research: The Journal of Laboratory and Clinical Medicine, 2018
460 citations recorded by the source corpus.
&NA; Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.
Subfields: Biophysics, Computer Vision and Pattern Recognition, Plant Science
View Paper: https://www.semanticscholar.org/paper/0472b28735371d52cbc4dc2d6303b67c8cda1b95
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
- Stefan Jaeger
- George R. Thoma
- Sameer Kiran Antani
- Mahdieh Poostchi
- Richard James Maude
- Kamolrat Silamut
In Computer Vision and Pattern Recognition
- George R. Thoma
- Dimpy Varshni
- Stefan Jaeger
- Sameer Kiran Antani
- Mahdieh Poostchi
- Kartik Thakral
- Ankush Mittal
- Lucky Agarwal
Citations in the corpus
- Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images (2018, methodology): “We applied a level-set based algorithm to detect and segment 170 the red blood cells (Poostchi et al., 2018).”
- Pneumonia Detection Using CNN based Feature Extraction (2019, methodology): “For this reason, most of the previous algorithms used hand crafted features for developing CAD systems based on examining images [1,2,3].”