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

PeerJ. 2018 Apr 16;6:e4568. doi: 10.7717/peerj.4568. eCollection 2018.

Abstract

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-trained CNN based 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.

Keywords: Blood smear; Computer-aided diagnosis; Convolutional Neural Networks; Deep Learning; Feature extraction; Machine Learning; Malaria; Pre-trained models; Screening.

Grant support

This work was supported in part by the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health (NIH) and the Lister Hill National Center for Biomedical Communications (LHNCBC). The NIH dictated study design, data collection/analysis, decision to publish and/or preparation of the manuscript. The Mahidol-Oxford Tropical Medicine Research Unit is funded by the Wellcome Trust of Great Britain. The Wellcome Trust of Great Britain had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.