|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.|
*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.