6 research outputs found
HCT: Hybrid Convnet-Transformer for Parkinson's disease detection and severity prediction from gait
In this paper, we propose a novel deep learning method based on a new Hybrid
ConvNet-Transformer architecture to detect and stage Parkinson's disease (PD)
from gait data. We adopt a two-step approach by dividing the problem into two
sub-problems. Our Hybrid ConvNet-Transformer model first distinguishes healthy
versus parkinsonian patients. If the patient is parkinsonian, a multi-class
Hybrid ConvNet-Transformer model determines the Hoehn and Yahr (H&Y) score to
assess the PD severity stage. Our hybrid architecture exploits the strengths of
both Convolutional Neural Networks (ConvNets) and Transformers to accurately
detect PD and determine the severity stage. In particular, we take advantage of
ConvNets to capture local patterns and correlations in the data, while we
exploit Transformers for handling long-term dependencies in the input signal.
We show that our hybrid method achieves superior performance when compared to
other state-of-the-art methods, with a PD detection accuracy of 97% and a
severity staging accuracy of 87%. Our source code is available at:
https://github.com/SafwenNaimiComment: 6 pages, 6 figures, 3 tables, Accepted for publication in IEEE
International Conference on Machine Learning and Applications (ICMLA),
copyright IEE
Automating lichen monitoring in ecological studies using instance segmentation of time-lapse images
Lichens are symbiotic organisms composed of fungi, algae, and/or
cyanobacteria that thrive in a variety of environments. They play important
roles in carbon and nitrogen cycling, and contribute directly and indirectly to
biodiversity. Ecologists typically monitor lichens by using them as indicators
to assess air quality and habitat conditions. In particular, epiphytic lichens,
which live on trees, are key markers of air quality and environmental health. A
new method of monitoring epiphytic lichens involves using time-lapse cameras to
gather images of lichen populations. These cameras are used by ecologists in
Newfoundland and Labrador to subsequently analyze and manually segment the
images to determine lichen thalli condition and change. These methods are
time-consuming and susceptible to observer bias. In this work, we aim to
automate the monitoring of lichens over extended periods and to estimate their
biomass and condition to facilitate the task of ecologists. To accomplish this,
our proposed framework uses semantic segmentation with an effective training
approach to automate monitoring and biomass estimation of epiphytic lichens on
time-lapse images. We show that our method has the potential to significantly
improve the accuracy and efficiency of lichen population monitoring, making it
a valuable tool for forest ecologists and environmental scientists to evaluate
the impact of climate change on Canada's forests. To the best of our knowledge,
this is the first time that such an approach has been used to assist ecologists
in monitoring and analyzing epiphytic lichens.Comment: 6 pages, 3 Figures, 8 Tables, Accepted for publication in IEEE
International Conference on Machine Learning and Applications (ICMLA),
copyright IEE