In this article, a method to fast classify (intradural hemorrhage, epidural hemorrhage, and cerebral parenchymal hemorrhage) and locate the bleeding points by using the singularity expansion method (SEM) and backpropagation (BP) neural network optimized by genetic algorithm (GA) and sparrow search algorithm (SSA) is proposed. In the simulation model, the bleeding spot with a radius of 3 mm is successfully identified by the approach. The test accuracy in the simulation for both the bleeding’s localization and classification are 98.0% and 97.4%, respectively. Head phantoms that have all been improved over the previous phantom established are used for experiments. A bleeding target with a volume of 3 mL can be identified in the microwave detection system. In the experiment, the accuracy of classification and localization of the bleeding type are 90% and 94.7%, respectively. The final results demonstrate the capability and effectiveness of the method. Faster determination of bleeding point type and orientation means that patients can be provided with different rescue measures accordingly.

A novel signal processing method to directly extract the hemorrhage response signals using the maximum correlation kurtosis deconvolution (MCKD) technique and complete ensemble empirical mode decomposition (CEEMDAN) algorithm is proposed in this article. This method only needs to acquire the raw data measured one time, which saves a lot of time in data acquisition. In emergency cases, a shorter time in detection means a better prognosis and better life for the patients. A portable microwave brain hemorrhage imaging system comprising a portable vector network analyzer (VNA), a small-sized antenna array (30 mm $\times$ 30.5 mm of each antenna), a flexible substrate, simple structure, and wideband (0.5–6 GHz) is established by applying this proposed signal processing method. The preliminary results from the experiments on the head phantom demonstrate the capability and effectiveness of this proposed method. Final results show that the proposed portable microwave imaging system can successfully detect brain hemorrhage with a size of 17 mm in diameter. The robustness of the proposed method is verified by successfully detecting the brain hemorrhage with a size of 20 mm in diameter.

Introduction: Emergency rescue scenes and pre-hospital emergency stages commonly encounter trauma victims. Life-saving measures must be taken at the scene if a trauma patient has pneumothorax; if the patient is not evaluated and diagnosed right away, their life may be in jeopardy. Ultrasound, which has the advantages of being non-invasive, non-radioactive, portable, rapid, and repeatable, can be used to diagnose pneumothorax. However, those who interpret ultrasound images must undergo extensive, specialized, and rigorous training. Deep learning technology allows for the intelligent diagnosis of ultrasound images, allowing general healthcare professionals to quickly and with minimal training diagnose pneumothorax in lung ultrasound patients.Methods: Previous studies focused primarily on the lung-sliding characteristics of M-mode images, neglecting other key features in lung ultrasonography pneumothorax, and used similar technological techniques. Our study team used video understanding technology for medical ultrasound imaging diagnostics, training the TSM video understanding model on the ResNet50 network with 657 clips and testing the model with untrained 164 lung ultrasound clips.Results: The model’s sensitivity was 99.21%, specificity was 89.19%, and average accuracy was 96.95%. The F1 score was 0.929, and the AUC was 0.97.Discussion: This study is the first to apply video understanding models to the multi-feature fusion diagnosis of pneumothorax, demonstrating the feasibility of using video understanding technology in medical image diagnosis.

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets—unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR)—reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods.