Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study
This work addresses the need for efficient diagnostic tools in healthcare, particularly for resource-limited settings, by providing a portable solution for detecting diseases like malaria, tuberculosis, and intestinal parasites, though it is incremental as it applies existing deep learning techniques to a new hardware context.
The paper tackles the problem of enabling low-power portable healthcare diagnostics by proposing an embedded-hardware-based implementation for microscopy diagnostic support, achieving laboratory expert-level accuracy with 6x more power efficiency and an inference time of ~3 ms/sample compared to conventional CPU-based methods.
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.