Automated image processing method for the diagnosis and classification of malaria on thin blood smears

By Nicholas E. Ross, Charles J. Pritchard, David M. Rubin, Adriano G. Dusé. Medical and Biological Engineering and Computing, 2006

311 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Plant Science

View Paper: https://www.semanticscholar.org/paper/6fc2799c1ede19261fc4da91134251389c1a5813

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. George R. Thoma
2. Dimpy Varshni
3. Stefan Jaeger
4. Sameer Kiran Antani
5. Mahdieh Poostchi
6. Kartik Thakral
7. Ankush Mittal
8. Lucky Agarwal

In Media Technology

1. Sameer Kiran Antani

Citations in the corpus

• Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images (2018, methodology): “A majority of image analysis-based computer-aided diagnosis (CADx) software use ML techniques with hand-engineered features for decision-making (Ross et al., 2006; Das et al., 2013; 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].”
• Image analysis and machine learning for detecting malaria (2018, background): “[107] Ross NE, Pritchard CJ, Rubin DM, Duse AG.”