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

PeerJ

article

Sivaramakrishnan Rajaraman¹  Sameer K. Antani¹  Mahdieh Poostchi¹  Kamolrat Silamut²  A. Hossain³  Richard J. Maude²  Stefan Jaeger¹  George R. Thoma¹ 

[1] Lister Hill National Center for Biomedical Communications, National Library of Medicine;Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University;Department of Medicine, Chittagong Medical Hospital;Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford;Harvard TH Chan School of Public Health, Harvard University

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

DOI  :  10.7717/peerj.4568

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY