AI-Assisted Dynamic Tissue Evaluation for Early Bowel Cancer Diagnosis Using a Vibrational Capsule
AI-Assisted Dynamic Tissue Evaluation for Early Bowel Cancer Diagnosis Using a Vibrational Capsule
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DOI：
10.1109/lra.2023.3251853
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发表时间：
2023-04
影响因子：
5.2
通讯作者：
K. O. Afebu;Jiyuan Tian;Y. Liu;E. Papatheou;S. Prasad
中科院分区：
文献类型：
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作者：
K. O. Afebu;Jiyuan Tian;Y. Liu;E. Papatheou;S. Prasad
由于肠癌的早期体征是受影响病变生物力学特性的变化，因此提出了用于早期肠癌诊断的AI辅助动态组织评估。动态信号从一个自推进的振动胶囊接触原位肠病变进行了处理和分析的功能，可能是指示病变的生物力学变化。不同组合的功能被用来开发不同的病变表征模型。监督分类，使用多层感知器（MLP）和堆栈包围网络（SE）进行了旁边的无监督分类，使用K均值聚类。SE基学习器包括支持向量机（SVM），决策树，朴素贝叶斯和随机森林。模拟测试数据的交叉验证表明，SE优于其复合基学习器，然而，SVM作为基学习器显示出产生大于90%的准确率的趋势。MLP在准确性和高性能模型数量方面优于SE，因此是实验验证期间使用的唯一监督网络，平均准确率为96.5%。对于无监督分类，模拟和实验数据都表明，病变最好分为两个类别，代表良性和恶性病变。
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, Naïve 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.