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

Understand

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.