Towards early bowel cancer detection: A data driven dynamic method of lesions characterisation using a robotic capsule

This study aims to improve early bowel cancer detection by introducing a non-visual approach to lesion characterisation, with a focus on precision and reliability. The study makes use of the sensitive dynamics of a robotic capsule self-propelling in the bowel to biomechanically characterise encountered lesions. The method incorporates a two-phase machine learning involving supervised regression as first phase and unsupervised clustering as final phase. The initial phase employs multi-layer perceptron (MLP), support vector regression, and Gaussian process regression to predict the stiffness of lesions, expressed as Young’s modulus (E) from capsule displacement signals. The final phase involving Gaussian mixture model (GMM) clustering achieved over 89 % accuracy for both simulation and experiment. The GMM demonstrated improved precision and reliability when incorporating predictions from more accurate regression models, such as MLP, though it struggled with high and closely related E-values.