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Open AccessArticle

Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images

by Weijun Hu 1,*, Yan Zhang 2 and Lijie Li 1,*
1
College of Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, UK
2
School of Physics, University of Electronic Science and Technology of China, Chengdu 610054, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(16), 3584; https://doi.org/10.3390/s19163584
Received: 19 June 2019 / Revised: 9 August 2019 / Accepted: 15 August 2019 / Published: 17 August 2019
(This article belongs to the Section Intelligent Sensors)
The fast progress in research and development of multifunctional, distributed sensor networks has brought challenges in processing data from a large number of sensors. Using deep learning methods such as convolutional neural networks (CNN), it is possible to build smarter systems to forecasting future situations as well as precisely classify large amounts of data from sensors. Multi-sensor data from atmospheric pollutants measurements that involves five criteria, with the underlying analytic model unknown, need to be categorized, so do the Diabetic Retinopathy (DR) fundus images dataset. In this work, we created automatic classifiers based on a deep convolutional neural network (CNN) with two models, a simpler feedforward model with dual modules and an Inception Resnet v2 model, and various structural tweaks for classifying the data from the two tasks. For segregating multi-sensor data, we trained a deep CNN-based classifier on an image dataset extracted from the data by a novel image generating method. We created two deepened and one reductive feedforward network for DR phase classification. The validation accuracies and visualization results show that increasing deep CNN structure depth or kernels number in convolutional layers will not indefinitely improve the classification quality and that a more sophisticated model does not necessarily achieve higher performance when training datasets are quantitatively limited, while increasing training image resolution can induce higher classification accuracies for trained CNNs. The methodology aims at providing support for devising classification networks powering intelligent sensors. View Full-Text
Keywords: convolutional neural network; images processing; multi-sensor; diabetic retinopathy convolutional neural network; images processing; multi-sensor; diabetic retinopathy
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Hu, W.; Zhang, Y.; Li, L. Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images. Sensors 2019, 19, 3584.

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