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An Autoencoder Convolutional Neural Network Framework for Sarcopenia Detection Based on Multi-frame Ultrasound Image Slices

Abstract : Multi-Frame classification applications are constituted by instances composed by a package of image frames, such as videos, which frequently require very high computational re-sources. Furthermore, when the input instances contain a large proportion of noise, then the incorporation of noise filtering pre-processing techniques are considered essential. In this work, we propose an AutoEncoder Convolutional Neural Network model for Multi-Frame input applications. The AutoEncoder model aims to reduce the huge dimensional size of the initial instances, compress useful information while simultaneously remove the noise from each frame. Finally, a Convolutional Neural Network classification model is applied on the new transformed and compressed data instances. As a case study scenario for the proposed framework, we utilize Ultrasound images (image slices/frames extracted from every patient via a portable ultrasound device) for Sarcopenia detection. Based on our experimental re-sults the proposed framework outperforms traditional approaches.
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https://hal.inria.fr/hal-03287711
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Submitted on : Thursday, July 15, 2021 - 6:12:30 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:11:11 PM

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Emmanuel Pintelas, Ioannis E. Livieris, Nikolaos Barotsis, George Panayiotakis, Panagiotis Pintelas. An Autoencoder Convolutional Neural Network Framework for Sarcopenia Detection Based on Multi-frame Ultrasound Image Slices. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.209-219, ⟨10.1007/978-3-030-79150-6_17⟩. ⟨hal-03287711⟩

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