Two-stage machine learning models for bowel lesions characterisation using self-propelled capsule dynamics

Abstract

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.