AI-assisted dynamic tissue evaluation for early bowel cancer diagnosis using a vibrational capsule
Abefu, KO; Tian, J; Liu, Y; et al.Papatheou, E; Prasad, S
Date: 3 March 2023
Article
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
With early sign of bowel cancer being changes
in affected lesions biomechanical properties, an AI-assisted
dynamic tissue evaluation is proposed for early bowel cancer
diagnosis. Dynamic signals from a self-propelled vibrational
capsule in contact with in-situ bowel lesions were processed and
analysed for features that may be ...
With early sign of bowel cancer being changes
in affected lesions biomechanical properties, an AI-assisted
dynamic tissue evaluation is proposed for early bowel cancer
diagnosis. Dynamic signals from a self-propelled vibrational
capsule in contact with in-situ bowel lesions were processed and
analysed for features that may be indicative of biomechanical
changes in the lesions. Different combinations of the features
were used to develop different lesion characterisation models.
Supervised classification using Multi-Layer Perceptron (MLP)
and Stacking Ensemble networks (SE) was carried out alongside
unsupervised classification using K-means clustering. The SE
base-learners comprised Support Vector Machine (SVM), Decision Tree, Na¨ıve Bayes and Random Forest. Cross-validation
on simulated test data showed that the SEs outperformed
their composite base-learners, however, SVM as a base-learner
showed tendency to yield greater than 90% accuracy. The
MLPs outperformed the SEs in accuracies and in numbers
of high-performance models, hence, were the only supervised
network used during experimental validation and they yielded
an average accuracy of 96.5%. For unsupervised classification,
both simulation and experimental data showed that the lesions
are best clustered into two categories representing benign and
malignant lesions.
Engineering
Faculty of Environment, Science and Economy
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