Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics

2016/08/09 by John A. Quinn, Rose Nakasi, Quinn, John A. +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Image Processing Techniques and Applications

paper · pdf · doi:10.48550/arxiv.1608.02989

openalex publication_date 2016/08/09 · openalex created_date 2016/09/30 · openalex updated_date 2026/07/28

Abstract

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.

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