Bicer Mustafa Berkan
Electrical and Electronics Engineering Department, Engineering Faculty, Tarsus University, 33400 Mersin, Turkey.
Diagnostics (Basel). 2023 Mar 1;13(5):930. doi: 10.3390/diagnostics13050930.
In this study, neurocomputational models are proposed for the acquisition of radar-based microwave images of breast tumors using deep neural networks (DNNs) and convolutional neural networks (CNNs). The circular synthetic aperture radar (CSAR) technique for radar-based microwave imaging (MWI) was utilized to generate 1000 numerical simulations for randomly generated scenarios. The scenarios contain information such as the number, size, and location of tumors for each simulation. Then, a dataset of 1000 distinct simulations with complex values based on the scenarios was built. Consequently, a real-valued DNN (RV-DNN) with five hidden layers, a real-valued CNN (RV-CNN) with seven convolutional layers, and a real-valued combined model (RV-MWINet) consisting of CNN and U-Net sub-models were built and trained to generate the radar-based microwave images. While the proposed RV-DNN, RV-CNN, and RV-MWINet models are real-valued, the MWINet model is restructured with complex-valued layers (CV-MWINet), resulting in a total of four models. For the RV-DNN model, the training and test errors in terms of mean squared error (MSE) are found to be 103.400 and 96.395, respectively, whereas for the RV-CNN model, the training and test errors are obtained to be 45.283 and 153.818. Due to the fact that the RV-MWINet model is a combined U-Net model, the accuracy metric is analyzed. The proposed RV-MWINet model has training and testing accuracy of 0.9135 and 0.8635, whereas the CV-MWINet model has training and testing accuracy of 0.991 and 1.000, respectively. The peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM) metrics were also evaluated for the images generated by the proposed neurocomputational models. The generated images demonstrate that the proposed neurocomputational models can be successfully utilized for radar-based microwave imaging, especially for breast imaging.
在本研究中，提出了用于使用深度神经网络（DNN）和卷积神经网络（CNN）获取基于雷达的乳腺肿瘤微波图像的神经计算模型。利用用于基于雷达的微波成像（MWI）的圆形合成孔径雷达（CSAR）技术，针对随机生成的场景生成了1000次数值模拟。这些场景包含每次模拟中肿瘤的数量、大小和位置等信息。然后，基于这些场景构建了一个包含1000个具有复数值的不同模拟的数据集。因此，构建并训练了一个具有五个隐藏层的实值DNN（RV-DNN）、一个具有七个卷积层的实值CNN（RV-CNN）以及一个由CNN和U-Net子模型组成的实值组合模型（RV-MWINet），以生成基于雷达的微波图像。虽然所提出的RV-DNN、RV-CNN和RV-MWINet模型是实值的，但MWINet模型被重新构建为具有复数值层（CV-MWINet），从而总共得到四个模型。对于RV-DNN模型，发现其在均方误差（MSE）方面的训练误差和测试误差分别为103.400和96.395，而对于RV-CNN模型，训练误差和测试误差分别为45.283和153.818。由于RV-MWINet模型是一个组合的U-Net模型，因此对其准确性指标进行了分析。所提出的RV-MWINet模型的训练准确率和测试准确率分别为0.9135和0.8635，而CV-MWINet模型的训练准确率和测试准确率分别为0.991和1.000。还针对所提出的神经计算模型生成的图像评估了峰值信噪比（PSNR）、通用质量指数（UQI）和结构相似性指数（SSIM）指标。生成的图像表明，所提出的神经计算模型可成功用于基于雷达的微波成像，尤其是乳腺成像。
