Point of care diagnostics using microscopy and computer vision methods have been applied to a number of practical problems, and are particularly relevant to low-income, high disease burden areas. However, this is subject to the limitations in sensitivity and specificity of the computer vision methods used. In general, deep learning has recently revolutionised the field of computer vision, in some cases surpassing human performance for other object recognition tasks. In this paper, we evaluate the performance of deep convolutional neural networks on three different microscopy tasks: diagnosis of malaria in thick blood smears, tuberculosis in sputum samples, and intestinal parasite eggs in stool samples. In all cases accuracy is very high and substantially better than an alternative approach more representative of traditional medical imaging techniques.
@article{arxiv.1608.02989,
title = {Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics},
author = {John A. Quinn and Rose Nakasi and Pius K. B. Mugagga and Patrick Byanyima and William Lubega and Alfred Andama},
journal= {arXiv preprint arXiv:1608.02989},
year = {2016}
}
Comments
Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, CA