A Stacked Autoencoder Neural Network Algorithm for Breast Cancer Diagnosis With Magnetic Detection Electrical Impedance Tomography
A Stacked Autoencoder Neural Network Algorithm for Breast Cancer Diagnosis With Magnetic Detection Electrical Impedance Tomography
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DOI：
10.1109/access.2019.2961810
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Huiquan
中科院分区：
文献类型：
--
作者：
Chen, Ruijuan;Wu, Weiwei;Wang, Huiquan
磁检测电阻抗断层成像(MDEIT)是一种新型的成像技术，其目的是通过电流注入和磁传感器测量外部磁通密度来重建电导率分布。为了提高MDEIT的分辨率和精度，为乳腺癌的诊断提供一种有效的成像方法，提出了一种基于堆叠式自动编码器(SAE)神经网络的新算法。通过数值模拟和体模实验验证了该方法的可行性。在数值模拟中，计算了不同电导率分布的大量样本数据。然后建立神经网络模型，通过训练样本对其进行训练。最后，利用该网络模型可以重建具有异常位置的成像目标的电导率分布。将SAE算法的重建结果与传统灵敏度矩阵(SM)算法和反向传播(BP)神经网络算法的重建结果进行了比较。在30dB噪声下，BP算法、SM算法和SAE算法的相对误差分别为137.19%、24.90%和15.28%。结果表明，SAE算法重建的异常位置更准确，电导率值更接近真实值，抗噪声能力更强。最后，在自制平台上完成了乳房模型实验，验证了新算法的应用可行性。提出的SAE算法可以将电导率的相对重建误差减小到14.56%。结果表明，通过SAE算法，MDEIT在乳腺癌的临床诊断中是一种很有前途的方法，也为MDEIT的广泛应用提供了更潜在的应用前景。
Magnetic detection electrical impedance tomography (MDEIT) is a novel imaging technique that aims to reconstruct the conductivity distribution with electrical current injection and the external magnetic flux density measurement by magnetic sensors. Aiming at improving the resolution and accuracy of MDEIT and providing an efficient imaging method for breast cancer diagnosis, a new algorithm based on stacked auto-encoder (SAE) neural network is proposed. Both numerical simulation and phantom experiments are done to verify its feasibility. In the numerical simulation, an amount of sample data with different conductivity distribution are calculated. Then a neural network model is established and trained by training these samples. Finally, the conductivity distribution of an imaging target with the anomaly location can be reconstructed by the network model. The reconstruction result of the SAE algorithm is compared with the reconstruction results of the traditional sensitivity matrix (SM) algorithm and the back propagation (BP) neural network algorithm. Under the noise of 30dB, the relative errors of BP algorithm, SM algorithm and SAE algorithm are 137.19%, 24.90% and 15.28% respectively. Result shows by the SAE algorithm, the location of anomalies is reconstructed more accurately, the conductivity value is more closely to the real one and the anti-noise performance is more robust. At last, a breast phantom experiment by self-made platforms is completed to verify the application feasibility of the new algorithm. The relative reconstruction error of conductivity by proposed SAE algorithm can be reduced to 14.56%. The results show that by SAE algorithm, MDEIT can be a promising approach in clinical diagnosis of breast cancer, and it also provide more potential application prospect for the extensive application of MDEIT.