Abstract:
Wireless capsule endoscopic (WCE) is a non-invasive device, introduced in 2000, that is used
by physicians in the diagnosis of diseases of the gastrointestinal (GI) tract. Despite these limited
resources, one capsule can operate for several hours and transmit tens of thousands of images.
However, these images are of low quality and frequency. In addition, this tool will not be able
to visualize the entire GI tract, as the battery life is limited. The main goal of the present work
is to develop algorithms for the automatic processing of these images, to help in the diagnosis
and in the reduction of the energy consumption. As a first contribution, we propose an automatic
classification of lesions and digestive organs in WCE images. This classification is achieved by
using two learning techniques such as learning from scratch of a proposed CNN and a transfer
learning of pre-trained CNNs. In a second step, we present a new classification method to
automatically detect different diseases of the (GI) tract. It is a deep learning algorithm based on
features concatenation of two pre-trained convolutional neural networks. In a second
contribution, we propose an intelligent compression scheme, which addresses the energy
limitation issues of WCE. The principle is to include a classification feedback loop, based on
deep learning, to determine the importance of transmitted images. This classification is used in
conjunction with a predictive compression algorithm to intelligently manage the limited energy
of the capsule. The goal of such a system is to increase the battery life or to obtain high quality
images in specific areas. Based on the results obtained, we conclude that our system is efficient
and provides good energy optimization of the capsule.
