ArticleSensors (Basel, Switzerland)2021
A Case Study of Quantizing Convolutional Neural Networks for Fast Disease Diagnosis on Portable Medical Devices.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Self-powered intelligence for personalized healthcare.National science review · 2026Review
- Efficacy of a Real-Time Artificial Intelligence Ultrasound System With Computer-Aided Detection and Diagnosis for Breast Cancer: A Feasibility Study.Journal of breast cancer · 2025Article
- A collaborative inference strategy for medical image diagnosis in mobile edge computing environment.PeerJ. Computer science · 2025Article
- Automated Machine Learning System for Defect Detection on Cylindrical Metal Surfaces.Sensors (Basel, Switzerland) · 2022Article
- Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks.Micromachines · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Recently, the amount of attention paid towards convolutional neural networks (CNN) in medical image analysis has rapidly increased since they can analyze and classify images faster and more accurately than human abilities. As a result, CNNs are becoming more popular and play a role as a supplementary assistant for healthcare professionals. Using the CNN on portable medical devices can enable a handy and accurate disease diagnosis. Unfortunately, however, the CNNs require high-performance computing resources as they involve a significant amount of computation to process big data. Thus, they are limited to being used on portable medical devices with limited computing resources. This paper discusses the network quantization techniques that reduce the size of CNN models and enable fast CNN inference with an energy-efficient CNN accelerator integrated into recent mobile processors. With extensive experiments, we show that the quantization technique reduces inference time by 97% on the mobile system integrating a CNN acceleration engine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.