TY  - CHAP
A1  - Wansing, Christian
A1  - Banos, Oresti
A1  - Pomares, Hector
A1  - Glösekötter, Peter
A1  - Rojas Ruiz, Ignacio
ED  - Junta de Andalucia Project P12-TIC-2082., Conference Paper
T1  - Development of a platform for the exchange of biodatasets with integrated opportunities for artificial intelligence using MatLab
N2  - This paper deals with the issue of automating the
process of machine learning and analyzing bio-datasets. For this
a user-friendly website has been developed for the interaction
with the researchers. On this website it is possible to upload
datasets and to share them, if desired, with other scientists. The
uploaded data can also be analyzed by various methods and
functions. The signals inside these datasets can also be visualized.
Furthermore several algorithms have been implemented to create
machine learning models with the uploaded data. Based on these
generated models new data can be classified or calculated. For all
these applications the simplest possible handling was
implemented to make the website available to all interested
researchers.
KW  - M-health, machine learning, website
Y1  - 2015
U6  - http://dx.doi.org/10.1109/ICoCS.2015.7483278
SP  - 1
EP  - 6
ER  - 
TY  - CHAP
A1  - Rörup, Tim
A1  - Glösekötter, Peter
A1  - Pomares, Hector
A1  - Ruiz, Ignacio
T1  - Deep Learning Based Neural Network for Six-Class-Classification of Alzheimer's Disease Stages Based on MRI Images
T2  - IWANN2021
N2  - State of the art classifiers split Alzheimer’s disease progression into a limited number of stages and use a comparatively small database. For the best treatment, it is desirable to have the highest resolution from the progression of the disease. This paper proposes a reliable deep convolutional neural network for the classification of six different Alzheimer’s disease stages based on Magnetic Resonance Imaging (MRI). The peculiarity of this paper is the introduction of a new, sixth, disease stage, and the large amount of data that has been taken into account. Additionally, not only the testing accuracy is analyzed, but also the robustness of the classifier to have feedback on how certain the neural network makes its predictions.
KW  - Deep Learning
Y1  - 2021
U6  - http://dx.doi.org/10.1007/978-3-030-85030-2_1
SP  - 3
EP  - 14
PB  - Springer International Publishing
ER  -