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Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library). [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been widely used in breast cancer diagnosis, and many high-performance models have emerged. However, most of the existing deep learning models are mainly based on static breast ultrasound (US) images. In actual diagnostic process, contrast-enhanced ultrasound (CEUS) is a commonly used technique by radiologists. Compared with static breast US images, CEUS videos can provide more detailed blood supply information of tumors, and therefore can help radiologists make a more accurate diagnosis. In this paper, we propose a novel diagnosis model based on CEUS videos. The backbone of the model is a 3D convolutional neural network. More specifically, we notice that radiologists generally follow two specific patterns when browsing CEUS videos. One pattern is that they focus on specific time slots, and the other is that they pay attention to the differences between the CEUS frames and the corresponding US images. To incorporate these two patterns into our deep learning model, we design a domain-knowledge-guided temporal attention module and a channel attention module. We validate our model on our Breast-CEUS dataset composed of 221 cases. The result shows that our model can achieve a sensitivity of 97.2% and an accuracy of 86.3%. In particular, the incorporation of domain knowledge leads to a 3.5% improvement in sensitivity and a 6.0% improvement in specificity. Finally, we also prove the validity of two domain knowledge modules in the 3D convolutional neural network (C3D) and the 3D ResNet (R3D). [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%–40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis. [Display omitted] • A new dual branch attention network (DBAN) is proposed. • The dual branch combines different attention mechanisms to achieve multi-level and multi-scale fusion. • The DBAN model achieves excellent classification performance for diabetic kidney disease diagnosis. [ABSTRACT FROM AUTHOR]