Two-stage machine learning models for bowel lesions characterisation using self-propelled capsule dynamics
Two-stage machine learning models for bowel lesions characterisation using self-propelled capsule dynamics
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DOI：
10.1007/s11071-023-08852-6
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发表时间：
2023-09
影响因子：
5.6
通讯作者：
K. O. Afebu;Jiyuan Tian;E. Papatheou;Y. Liu;S. Prasad
中科院分区：
文献类型：
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作者：
K. O. Afebu;Jiyuan Tian;E. Papatheou;Y. Liu;S. Prasad
为了促进肠癌的早期诊断，提出了肠病变的非侵入性生物力学表征。该方法利用自推进胶囊的动力学和两阶段机器学习过程。当胶囊行进并遇到肠道病变时，其表现出的动力学被认为具有生物力学意义，是一个高度敏感的非线性动力学系统。在本研究中，对可测量的胶囊动力学（包括加速度和位移）进行了分析，以寻找可能指示生物力学差异的特征，在本例中为杨氏模量。机器学习的第一阶段涉及监督回归网络的开发，包括多层感知器（MLP）和支持向量回归（SVR），它们能够根据动态信号特征预测杨氏模量。第二阶段涉及使用 K 均值聚类将预测的杨氏模量无监督地分类为具有高簇内相似性但低簇间相似性的簇。基于确定系数和归一化平均绝对误差等性能指标，MLP 模型在测试数据上表现出比 SVR 更好的性能。对于位移和加速度均可测量的情况，基于位移的模型优于基于加速度的模型。因此，这些结果使得胶囊位移和 MLP 网络成为所提出的肠道病变表征和早期肠癌诊断的一线选择。
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 encounters 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 acceleration and displacement have been analysed for features that may be indicative of biomechanical differences, Young’s modulus in this case. The first stage of the machine learning involves the development of supervised regression networks including multi-layer perceptron (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.