Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection

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 deman...

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Main Authors: Lu, C., Liew, W.S., Tang, T.B., Lin, C.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:http://scholars.utp.edu.my/id/eprint/34330/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301246&doi=10.1109%2fLES.2023.3234973&partnerID=40&md5=21ca087f3e2ccd1abc4e6f6c19d424b7
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spelling oai:scholars.utp.edu.my:343302023-02-17T12:58:30Z http://scholars.utp.edu.my/id/eprint/34330/ Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection Lu, C. Liew, W.S. Tang, T.B. Lin, C. 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 fulfil 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 2.5488mW at a working frequency of 8MHz. We conclude this letter with a discussion of the results and future direction of research. IEEE Institute of Electrical and Electronics Engineers Inc. 2023 Article NonPeerReviewed Lu, C. and Liew, W.S. and Tang, T.B. and Lin, C. (2023) Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection. IEEE Embedded Systems Letters. p. 1. ISSN 19430663 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301246&doi=10.1109%2fLES.2023.3234973&partnerID=40&md5=21ca087f3e2ccd1abc4e6f6c19d424b7 10.1109/LES.2023.3234973 10.1109/LES.2023.3234973 10.1109/LES.2023.3234973
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
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 fulfil 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 2.5488mW at a working frequency of 8MHz. We conclude this letter with a discussion of the results and future direction of research. IEEE
format Article
author Lu, C.
Liew, W.S.
Tang, T.B.
Lin, C.
spellingShingle Lu, C.
Liew, W.S.
Tang, T.B.
Lin, C.
Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection
author_facet Lu, C.
Liew, W.S.
Tang, T.B.
Lin, C.
author_sort Lu, C.
title Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection
title_short Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection
title_full Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection
title_fullStr Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection
title_full_unstemmed Implementation of a Convolutional Neural Network into an Embedded Device for Polyps Detection
title_sort implementation of a convolutional neural network into an embedded device for polyps detection
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/34330/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301246&doi=10.1109%2fLES.2023.3234973&partnerID=40&md5=21ca087f3e2ccd1abc4e6f6c19d424b7
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