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Vol. 34, No. 8(3), S&M3042

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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 32, Number 10(3) (2020)
Copyright(C) MYU K.K.

pp. 3517-3530
S&M2355 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2933
Published: October 30, 2020

Computed Tomography Image Recognition with Convolutional Neural Network Using Wearable Sensors [PDF]

Yuqing He, Lei Lei, Guangsong Yang, Chih-Cheng Chen, Christopher Chun Ki Chan, and Kuei-Kuei Lai

(Received April 22, 2020; Accepted August 12, 2020)

Keywords: convolutional neural network, CT image recognition, diagnosis support, overfitting

We propose a modified convolutional neural network (CNN) tailor-made for computed tomography (CT) image disease recognition to assist doctors in disease diagnosis. First, we analyze the effects of varying the CNN activation function and pooling parameters, as well as the CNN’s performance using one data set. Second, we address the activation error that occurs when the sample data size is increased by preprocessing images by an enhancement technique, adjusting the activation function and initialization weighting, training/testing the target, and adaptively extracting features. We found that our method alleviates overfitting with these techniques. The experimental results show that our proposed scheme improves the recognition rate and can better generalize findings.

Corresponding author: Guangsong Yang, Chih-Cheng Chen

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Yuqing He, Lei Lei, Guangsong Yang, Chih-Cheng Chen, Christopher Chun Ki Chan, and Kuei-Kuei Lai, Computed Tomography Image Recognition with Convolutional Neural Network Using Wearable Sensors, Sens. Mater., Vol. 32, No. 10, 2020, p. 3517-3530.