Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

• Published in: PeerJ (San Francisco, CA) Vol. 6; p. e4568
• Main Authors: Rajaraman, Sivaramakrishnan, Antani, Sameer K., Poostchi, Mahdieh, Silamut, Kamolrat, Hossain, Md. A., Maude, Richard J., Jaeger, Stefan, Thoma, George R.
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 16.04.2018; PeerJ, Inc; PeerJ Inc
• Subjects: Analysis; Artificial intelligence; Artificial neural networks; Automation; Blood; Blood smear; Computational Science; Convolutional Neural Networks; Data Mining and Machine Learning; Data Science; Decision making; Deep Learning; Diagnosis; Diagnostic imaging; Disease transmission; Health aspects; Health screening; Image processing; Infectious Diseases; Learning algorithms; Machine Learning; Malaria; Medicine; Methods; Neural networks; Parasitemia; Parasites; Plasmodium falciparum; Pre-trained models; United States > US; Screening; Blood smear; Malaria; Pre-trained models; Feature extraction; Convolutional Neural Networks; Machine Learning; Computer-aided diagnosis; Deep Learning

Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifyi...