mlsanityData to 14 Oct 2026

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

arXiv 2209.01507 ↗First posted 3 Sep 2022cs.LG cs.ARcited by 0

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Abstract (arXiv)

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 ~ 3 ms/sample.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 3 Sep 2022.

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References · 7

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  3. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  4. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 288
  5. 2016Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnosticscited by 6
  6. 2020Design Space Exploration of Algorithmic Multi-Port Memories in High-Performance Application-Specific Acceleratorscited by 2
  7. 2020NV-Fogstore : Device-aware hybrid caching in fog computing environmentscited by 2

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