  Deep learning research has generated widespread interest leading to emergence
of a large variety of technological innovations and applications. As
significant proportion of deep learning research focuses on vision based
applications, there exists a potential for using some of these techniques to
enable low-power portable health-care diagnostic support solutions. In this
paper, we propose an embedded-hardware-based implementation of microscopy
diagnostic support system for PoC case study on: (a) Malaria in thick blood
smears, (b) Tuberculosis in sputum samples, and (c) Intestinal parasite
infection in stool samples. We use a Squeeze-Net based model to reduce the
network size and computation time. We also utilize the Trained Quantization
technique to further reduce memory footprint of the learned models. This
enables microscopy-based detection of pathogens that classifies with laboratory
expert level accuracy as a standalone embedded hardware platform. The proposed
implementation is 6x more power-efficient compared to conventional CPU-based
implementation and has an inference time of $\sim$ 3 ms/sample.
