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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/44916
Title: Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Authors: Sivaramakrishnan Rajaraman
Sameer K. Antani
Mahdieh Poostchi
Kamolrat Silamut
Md A. Hossain
Richard J. Maude
Stefan Jaeger
George R. Thoma
Harvard School of Public Health
Mahidol University
Nuffield Department of Clinical Medicine
National Library of Medicine
Chittagong General Hospital
Keywords: Agricultural and Biological Sciences;Biochemistry, Genetics and Molecular Biology;Neuroscience
Issue Date: 1-Jan-2018
Citation: PeerJ. Vol.2018, No.4 (2018)
Abstract: © 2018 Rajaraman et al. 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 classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer- aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trainedCNNbased DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85045518625&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/44916
ISSN: 21678359
Appears in Collections:Scopus 2018

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