Hosseini Seyed Mohsen, Amiri Mahmood, Najarian Siamak, Dargahi Javad
Biomechanics Department, Laboratory of Artificial Tactile Sensing and Robotic Surgery, Faculty of Biomedical Engineering, Amirkabir University of Technology, Hafez Avenue, Tehran, Iran.
Int J Med Robot. 2007 Sep;3(3):235-44. doi: 10.1002/rcs.138.
Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce 'forward results'.
Three feed-forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back-propagation (RP) algorithm with a leave-one-out (LOO) cross-validation approach is used for training purposes.
The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.
There is a non-linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small.
人工触觉传感是一种可检测生物组织中肿瘤的存在并利用计算机化逆分析得出“正向结果”的方法。
已开发出三个前馈神经网络（FFNN）用于估计肿瘤特征。每个网络提供肿瘤的三个参数之一，即直径、深度和肿瘤与组织的硬度比。采用带留一法（LOO）交叉验证方法的弹性反向传播（RP）算法进行训练。
所提出的基于神经网络的逆方法是用于诊断测试的可靠且高效的工具，以便准确估计组织中肿瘤的基本参数。
肿瘤特征与其对提取特征的影响之间存在非线性相关性。一般来说，当肿瘤深度较小时，可获得对肿瘤硬度的可靠估计。
