Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study

• URL: http://arxiv.org/abs/2209.01507v1
• Date: Sat, 3 Sep 2022 22:41:52 GMT
• Title: Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study
• Authors: Khushal Sethi, Vivek Parmar and Manan Suri
• Abstract summary: We propose an embedded-hardware-based implementation of microscopy diagnostic support system for PoC case study. We use a Squeeze-Net based model to reduce the network size and time. We also utilize the Trained Quantization technique to further reduce memory footprint of the learned models.
• Score: 6.011991689754301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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