Automated malaria diagnosis using convolutional neural networks in an on-field setting

Automated malaria diagnosis using convolutional neural networks in an on-field setting: The analysis of low quality smartphone based microscope images

Sorgedrager, Riemer (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control)

Verhaegen, M.H.G. (mentor)
Agbana, T.E. (graduation committee)
Bliek, L. (graduation committee)
Vdovin, Gleb (graduation committee)
Baldi, S. (graduation committee)
Kober, J. (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

OSMD

2018-01-23

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone microscope system compared to traditional high-end light microscopes, conventional algorithms fail to process these images. We propose a framework using a convolutional neural network as a pixel classifier to localize the erythrocytes. Afterwards we classify them accordingly, using a convolutional neural network as an object classifier. Such a system can offer in the-field malaria diagnosis without human intervention or can act as an aid for human experts to lower workload and increase diagnosis accuracy. The algorithm successfully localizes the erythrocytes with an average sensitivity of 97.31% and precision of 92.21%. Classification performed inadequate, in terms of low agreement with two human experts. This can be due to the low image quality or the small amount of training data available at the time.

Malaria Detection
Convolutional Neural Networks
Artifical neural networks
Deep learning
Localization
Classification
smartphone based microscope
Automated

http://resolver.tudelft.nl/uuid:7def1a99-8a56-4875-8dfd-73e57869dec1

Student theses

master thesis

© 2018 Riemer Sorgedrager