This is the author accepted manuscript.Data accessibility:
The data sets generated and analysed during the cur rent study are available from the corresponding author
on reasonable request.To foster early bowel cancer diagnosis, a
non-invasive biomechanical characterisation of bowel
lesions is proposed. This method uses the dynamics
of a self-propelled capsule and a two-stage machine
learning procedure. As the capsule travels and encoun ters lesions in the bowel, its exhibited dynamics are
envisaged to be of biomechanical significance being a
highly sensitive nonlinear dynamical system. For this
study, measurable capsule dynamics including accel eration and displacement have been analysed for fea tures that may be indicative of biomechanical differ ences, Young’s modulus in this case. The first stage of
the machine learning involves the development of su pervised regression networks including multi-layer per ceptron (MLP) and support vector regression (SVR),
that are capable of predicting Young’s moduli from dynamic signals features. The second stage involves an
unsupervised categorisation of the predicted Young’s
moduli into clusters of high intra-cluster similarity but
low inter-cluster similarity using K-means clustering.
Based on the performance metrics including coefficient
of determination and normalised mean absolute error, the MLP models showed better performances on
the test data compared to the SVR. For situations
where both displacement and acceleration were measurable, the displacement-based models outperformed
the acceleration-based models. These results thus make
capsule displacement and MLP network the first-line choices for the proposed bowel lesion characterisation
and early bowel cancer diagnosis.Engineering and Physical Sciences Research Council (EPSRC
