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: | , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
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| 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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| Summary: | 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 |
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