Breast tumor detection by 1D-convolutional neural network based on ultra-wide-band microwave technology

Abstract

Abstract Ultra-wide band (UWB) microwave is a promising technology for non-invasive detection of breast tumors due to its low cost and the absence of ionizing radiation. However, breast heterogeneity is still the main technical barrier of microwave imaging at present. It is challenging to identify whether there are tumor responses from the detected microwave signals because they contain many high-magnitude skin and fibroglandular scattering signals. To this end, a 1D-convolution neural network (CNN) is proposed to detect the presence of breast tumors directly from raw time-domain microwave signals, avoiding complex microwave image reconstruction and time-consuming feature engineering. The designed network has 10 weight layers; it is composed of a stack of convolution-rectified linear unit-batch normalization layers, and every two stacks are followed by a maxpooling layer. At the end of the network are two fully connected layers to complete the detection task. With the proposed network, the detection accuracies on two datasets simulated by an electromagnetic solver reach 98.20% and 95.97%. The accuracy of experimental verification based on the practical UWB detection system remains good, reaching 94.66%. Finally, through the t-distributed stochastic neighbor embedding technique, the results of feature visualization prove the effectiveness of the trained CNN model. The evaluation results indicate the superior performance of the proposed approach for UWB microwave-based breast tumor detection.