Stroke is a leading cause of mortality and disability. Emergent diagnosis and
intervention are critical, and predicated upon initial brain imaging; however,
existing clinical imaging modalities are generally costly, immobile, and demand
highly specialized operation and interpretation. Low-energy microwaves have
been explored as low-cost, small form factor, fast, and safe probes of tissue
dielectric properties, with both imaging and diagnostic potential.
Nevertheless, challenges inherent to microwave reconstruction have impeded
progress, hence microwave imaging (MWI) remains an elusive scientific aim.
Herein, we introduce a dedicated experimental framework comprising a robotic
navigation system to translate blood-mimicking phantoms within an anatomically
realistic human head model. An 8-element ultra-wideband (UWB) array of modified
antipodal Vivaldi antennas was developed and driven by a two-port vector
network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex
scattering parameters were measured, and dielectric signatures of hemorrhage
were learned using a dedicated deep neural network for prediction of hemorrhage
classes and localization. An overall sensitivity and specificity for detection
>0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study
establishes the feasibility of a robust experimental model and deep learning
solution for UWB microwave stroke detection.

中风是致死和致残的主要原因。该研究介绍了一种利用超宽带微波成像和深度学习解决方案进行中风检测的可行实验模型。

使用深度学习的超宽带微波出血检测和定位实验系统

An experimental system for detection and localization of hemorrhage  using ultra-wideband microwaves with deep learning

Objective: This paper proposes a deep learning model for breast cancer
detection from reconstructed images of microwave imaging scan data and aims to
improve the accuracy and efficiency of breast tumor detection, which could have
a significant impact on breast cancer diagnosis and treatment. Methods: Our
framework consists of different convolutional neural network (CNN)
architectures for feature extraction and a region-based CNN for tumor
detection. We use 7 different architectures: DenseNet201, ResNet50,
InceptionV3, InceptionResNetV3, MobileNetV2, NASNetMobile and NASNetLarge and
compare its performance to find the best architecture out of the seven. An
experimental dataset of MRI-derived breast phantoms was used. Results:
NASNetLarge is the best architecture which can be used for the CNN model with
accuracy of 88.41% and loss of 27.82%. Given that the model's AUC is 0.786, it
can be concluded that it is suitable for use in its present form, while it
could be improved upon and trained on other datasets that are comparable.
Impact: One of the main causes of death in women is breast cancer, and early
identification is essential for enhancing the results for patients. Due to its
non-invasiveness and capacity to produce high-resolution images, microwave
imaging is a potential tool for breast cancer screening. The complexity of
tumors makes it difficult to adequately detect them in microwave images. The
results of this research show that deep learning has a lot of potential for
breast cancer detection in microwave images

本文旨在提供一种基于 CNN 和深度学习的乳腺癌检测模型，该模型通过重建微波成像扫描数据并使用多种不同的 CNN 架构进行特征提取和肿瘤检测，最终得出使用 NASNetLarge 模型的 CNN 模型具有较高准确率和较低损失的结论，为微波成像技术在乳腺癌筛查中的应用提供了新的研究方向。