relation: https://khub.utp.edu.my/scholars/19887/
title: Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection
creator: Lu, C.-K.
creator: Liew, W.S.
creator: Tang, T.B.
creator: Lin, C.-H.
description: The increasing rates of colorectal cancer and associated mortality have attracted interest in the use of computer-aided diagnosis tools based on artificial intelligence (AI) for the detection of polyps at an early stage. Most AI models are implemented on software platforms; however, due to the demands of embedded devices, hardware implementations have to fulfill the demands of real-time applications with better accuracy and low-power consumption. In this letter, we propose an optimized four-layer network that can be implanted into an embedded device and determine the feasibility of implanting our convolutional neural network (CNN) into a microprocessor. The essential functions of the CNN (i.e., padding, convolution, ReLU, max-pooling, fully connected, and softmax layers) are implemented in the microprocessor. The proposed method achieves efficient classification with high performance and takes only 2.5488 mW at a working frequency of 8 MHz. We conclude this letter with a discussion of the results and future direction of research. © 2009-2012 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024
type: Article
type: PeerReviewed
identifier:   Lu, C.-K. and Liew, W.S. and Tang, T.B. and Lin, C.-H.  (2024) Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection.  IEEE Embedded Systems Letters, 16 (1).  pp. 5-8.  ISSN 19430663     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301246&doi=10.1109%2fLES.2023.3234973&partnerID=40&md5=21ca087f3e2ccd1abc4e6f6c19d424b7
relation: 10.1109/LES.2023.3234973
identifier: 10.1109/LES.2023.3234973