Research on modulation recognition method of multi‐component radar signals based on deep convolution neural network

Acquiring the growth information of tobacco plants quickly and accurately is an important prerequisite for their daily management. In view of the problem that the convolutional neural network is easily affected by background information, this study introduces the Convolutional Block Attention Module into the U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2-</sup> Net. This method is mainly composed of an encoder and a decoder. Before the feature map is decoded, it will go through the Convolutional Block Attention Module to filter more important features and inhibit the invalid features. In the decoder, in order to fuse the features of objects in different scales, the feature maps in the decoder are up-sampled and then concatenated. This information aggregation solves the problem of difficult object detection at different scales to some extent. The results of this method are compared with four current mainstream deep learning models(U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net, DeeplabV3+, PSP-Net, U-Net). The result shows: 1) The U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net network based on the Convolutional Block Attention Module has the highest extraction accuracy for the UAV visible light remote senseing of tobacco(accuracy was 98.53%, recall was 96.00%,F1-Score was 96.38%, kappa coefficient was 0.95, IOU was 93.02%); 2)In the extraction of the three test areas, the extraction value of this method is highly consistent with the label area value(the area ratios of a total of 13 plants in the three regions were kept between 90% and 100%),the effect is much higher than that of DeeplabV3+ and PSP-Net, and slightly higher than the extraction effect of U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net and U-Net. 3)In the segmentation of three typical canopy closure regions of A, B, and C, most of the accuracy factors in different regions can reach 94% and above. It can be seen that the method in this study has certain advantages in feature extraction and has certain value in practical application.

In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabilitation training strategies, this paper constructs a multi-scale convolutional neural network (MSCNN) - convolutional block attention module (CBAM) - bidirectional long short-term memory network (BiLSTM) muscle strength prediction model to fully explore the spatial and temporal features of the data and simultaneously suppress useless features, and finally achieve the improvement of the accuracy of the muscle strength prediction model. To verify the effectiveness of the model proposed in this paper, the model in this paper is compared with traditional models such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), CNN - squeeze excitation network (SENet), MSCNN-CBAM and MSCNN-BiLSTM, and the effect of muscle strength prediction by each model is investigated when the hand force application changes from 40% of the maximum voluntary contraction force (MVC) to 60% of the MVC. The research results show that as the hand force application increases, the effect of the muscle strength prediction model becomes worse. Then the ablation experiment is used to analyze the influence degree of each module on the muscle strength prediction result, and it is found that the CBAM module plays a key role in the model. Therefore, by using the model in this article, the accuracy of muscle strength prediction can be effectively improved, and the characteristics and laws of hand muscle activities can be deeply understood, providing assistance for further exploring the mechanism of hand functions .

Brain-computer interfaces (BCI) allow for the brain to communicate with electronic devices. Concerning the BCI paradigms, motor imagery uses brain signals to decode an imagined movement. However, this is a hard task given the low signal-to-noise ratio. Usually, the main steps in BCI models are pre-processing, feature extraction, and classification. In recent years, Convolutional Neural Networks (CNNs) have been gaining relevance in several areas of science due to their feature extraction, translation invariance, and parameter sharing capa-bilities. Another, more recent way of feature extraction is using attention mechanisms, which are layers of neural networks based on human attention and have the ability to highlight important features. A variation of the attention mechanism is the Convolutional Block Attention Module, which combines the CNN structure with the attention mechanism. In this work, we propose a new model that joins the core architecture of EEGNet, a compact CNN widely used in the literature, with the Convolutional Block Attention Module and residual connections. The residual connections were introduced to lower data degradation throughout the model. The results highlight the residual connection's importance for the performance of the model. The proposed model obtained a kappa result 5.2% better than the EEGNet with a p-value less than 0.01 on BCI Competition IV dataset 2a, which is a well-known dataset for Motor Imagery. Furthermore, the proposal was better than EEGNet for most subjects and had the best-worst case.

MRI image classification of brain tumors is critical for accurate and early diagnosis. New developments in deep learning have revealed that inserting attention mechanisms into convolutional neural networks can greatly improve classification performance. This work assesses the effectiveness of Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) integrated with the ResNet50 model for brain tumor classification. The SE module recalibrates channel-wise feature responses, while CBAM enhances feature representation by combining channel and spatial attention. The SE and CBAM are combined with ResNet50, greatly increasing classification accuracy, precision, and recall when compared to the basic model, according to experimental results on MRI datasets. The suggested model employs attention mechanisms to focus valuable information selectively and suppress irrelevant information. The experiments on publicly available brain tumor datasets display great improvements over basic CNN models, with precision, recall, accuracy, as well as F1 score at 0.9914, 0.9903, 0.9945, and 0.9908, respectively. From these results, the importance of attention mechanisms in deep learning models for medical imaging is highlighted, which suggests that SE and CBAM modules can be available as more dependable and effective instruments for brain tumor classification in clinical settings. Future studies should investigate transformer-based and hybrid attention techniques to enhance automated brain tumor categorization.

Weeds are one of the primary factors that reduce crop productivity by competing for nutrients and water, causing the plant to lose weight and resulting in reduced grain yield. Traditional agricultural practices often rely on uniform herbicide application, which can contaminate soil and raise costs. On agricultural land, selective weed treatment are an efficient and cost-effective way to control weeds that require a deep learning-based crop and weed segmentation system. Many existing crop and weed segmentation research works focus on achieving precise crop and weed segmentation results, rather than building lightweight models to deploy on edge devices. To attain this, we develop an effective and efficient convolutional neural network, namely the Concatenated Attention U-Net with Convolutional Block Attention Module (CAUC). By integrating Linear Concatenated Blocks (LCB), Attention Gate (AG) connections, and Convolutional Block Attention Module (CBAM), the proposed model efficiently utilizes feature maps among its architectural components to achieve superior performance. Depth-wise convolution layers and 1 × 1 convolution layers in LCBs reduce computational complexity. To enable the proposed model to identify the weed portions in multiple crop fields, we integrated three datasets in this research work, namely the Crop/Weed Field Image Dataset (CWFID), Sugar Beet, and Sunflower datasets. Experimental results on carrot, sugar beet, and sunflower crop datasets demonstrate high Accuracy (99.09%), MIoU (81.02%), and F1-score (99.06%), with a modest model size (5.6 MB) and computational parameters (0.377 million). We developed a lightweight computer vision application (13.7 MB) to demonstrate the model’s efficacy on low-computational devices.

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

Early detection of breast cancer is crucial for effective treatment, making reliable screening methods essential for identifying initial symptoms.Various imaging techniques, such as mammography, ultrasound, and thermography, are commonly used for screening breast cancer.Traditional diagnostic methods, have significantly contributed to early detection but are often hampered by limitations, including false positives and false negatives, which can lead to unnecessary procedures or delayed treatment.This study proposes a model based on the ResNet50 Convolutional Neural Network (CNN) to detect breast cancer from mammograms, incorporating the Convolutional Block Attention Module (CBAM) as an attention mechanism.The model explores varying numbers of CBAM blocks to determine the optimal configuration while mitigating the risk of overfitting.The performance of the proposed model was evaluated using the dataset obtained from the PatchCamelyon (PCam) dataset by comparing its Area Under the Curve (AUC) scores with those of existing models.The results demonstrate that the ResNet50 model integrated with CBAM blocks shows improved performance with an increased number of blocks, with the model utilizing 8 CBAM blocks achieving the highest performance in terms of AUC, precision, recall, and accuracy.Compared to existing models, the proposed ResNet50 model with 8 CBAM blocks outperformed all others across all performance metrics, confirming the effectiveness of the CBAM integration and the overall model architecture in improving breast cancer detection.

The industrial smoke scene is complex and diverse, and the cost of labeling a large number of smoke data is too high. Under the existing conditions, it is very challenging to efficiently use a large number of existing scene annotation data and network models to complete the image classification and recognition task in the industrial smoke scene. Traditional deep learn-based networks can be directly and efficiently applied to normal scene classification, but there will be a large loss of accuracy in industrial smoke scene. Therefore, we propose a novel generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition. In this paper, a low-cost data enhancement method is used to effectively reduce the difference in the pixel field of the image. The smoke image is input into the LSTM in generator and encoded as a hidden layer vector. This hidden layer vector is then entered into the discriminator. Meanwhile, a convolutional block attention module is integrated into the discriminator to improve the feature self-extraction ability of the discriminator model, so as to improve the performance of the whole smoke image recognition network. Experiments are carried out on real diversified industrial smoke scene data, and the results show that the proposed method achieves better image classification and recognition effect. In particular, the F scores are all above 89%, which is the best among all the results.

In recent years, convolutional neural networks (CNNs) have been increasingly leveraged for the classification of hyperspectral imagery, displaying notable advancements. To address the issues of insufficient spectral and spatial information extraction and high computational complexity in hyperspectral image classification, we introduce the MDRDNet, an integrated neural network model. This novel architecture is comprised of two main components: a Multiscale 3D Depthwise Separable Convolutional Network and a CBAM-augmented Residual Dilated Convolutional Network. The first component employs depthwise separable convolutions in a 3D setting to efficiently capture spatial–spectral characteristics, thus substantially reducing the computational burden associated with 3D convolutions. Meanwhile, the second component enhances the network by integrating the Convolutional Block Attention Module (CBAM) with dilated convolutions via residual connections, effectively counteracting the issue of model degradation. We have empirically evaluated the MDRDNet’s performance by running comprehensive experiments on three publicly available datasets: Indian Pines, Pavia University, and Salinas. Our findings indicate that the overall accuracy of the MDRDNet on the three datasets reached 98.83%, 99.81%, and 99.99%, respectively, which is higher than the accuracy of existing models. Therefore, the MDRDNet proposed in this study can fully extract spatial–spectral joint information, providing a new idea for solving the problem of large model calculations in 3D convolutions.

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images

This study focuses on the optimization and practice of Convolutional Neural Networks (CNN) in image classification tasks. A deep neural network model was constructed by introducing the CBAM (Convolutional Block Attention Module) attention mechanism and residual structure. This model combines the stability of residual networks during training with the advantages of CBAM in feature extraction, aiming to improve image classification performance. The experiment used the CIFAR-10 dataset and achieved a testing accuracy of 86% through optimization methods such as data augmentation, batch normalization, reasonable control of the number of fully connected layers, and exponential decay learning rate strategy. The study also explored the effects of data augmentation, batch normalization layer utility, fully connected layer quantity control, residual structure effectiveness, and CBAM attention mechanism on model performance, providing valuable insights for optimizing CNN in image classification tasks.

For breast cancer, a malignant tumor developing from breast epithelial tissue, the limitations of traditional diagnostic methods (e.g., diagnostic errors and invasiveness) create critical challenges, underscoring the urgent need for artificial intelligence-assisted technological development. This paper systematically reviews the applications of Convolutional Neural Networks (CNNs) across the entire workflow of breast cancer, including screening, diagnosis, and prognosis, with a focus on hybrid CNNs such as architectures combining Transformer/Long Short-Term Memory (LSTM) and CNN-Support Vector Machine (SVM) models and attention-based CNNs such as contour-enhanced attention and cross-attention mechanisms. It analyzes how these models automate feature extraction from medical images to achieve breast lesion detection, benign-malignant differentiation, and prognosis prediction. Results show that hybrid models like Fusion of Hybrid Deep Features (FHDF) achieve over 98% classification accuracy on datasets such as MIAS by fusing features from multiple CNNs, while attention-based models like Convolutional Block Attention Module (CBAM)-Xception attain an Area Under Curve (AUC) of 0.97 and an accuracy of 89.1% in differentiating benign and malignant lesions. However, challenges remain, including insufficient interpretability, cross-institutional data heterogeneity, and privacy risks. The study proposes integrating medical expert systems and applying transfer learning and domain adaptation techniques to enhance model reliability and generalizability, promoting the translation of CNN technologies into clinical practice and constructing a precise and trustworthy AI-driven breast cancer diagnosis and treatment system.

In recent years, Sound Event Classification (SEC) is a remarkable field of computer audition research. The abilities to automatically capture and classify sound events are meaningful for expanding the auditory ability of autonomous vehicle in a complex driving environment. The Autonomous vehicles will encounter some special road conditions during driving, such as large truck occlusion, tunnels, and buildings that hinder radar detection. Therefore, the perception capabilities of on-board lidar, vision, and other sensors are impacted, resulting in a great threat to the safe driving of the car. At this time, auditory environment perception can provide auditory dimension information for the vehicle. However, with the increase of noise level, the SEC task of autonomous vehicle becomes seriously difficult. Traditional machine learning methods show weak robustness in the highly noisy environment. In this work, an autonomous vehicle Sound Event Classification (AVSEC) framework based on Spectrogram Image Features (SIFs) and Convolutional Neural Networks (CNN) algorithm is proposed to solve the SEC question. Further on, the ambulance, bus, civil defense siren, fire truck, screaming, etc. are taken as the research traffic sounds. Traffic Sound Datasets (TSD) are collected, augmented and customized. Then, directly connected channel (DCC) between convolutional blocks and Convolutional Block Attention Module (CBAM) are designed to optimize the neural network. Testing results show that the AVSEC system achieves an accuracy of 97.18%. Furthermore, the results of AVSEC and other machine learning methods are compared, which shows that the combination of SIFs and AVSEC-net provides better noise robustness and classification accuracy in AVSEC task. The sound classification algorithm designed in this paper, as an advanced auxiliary driving system for sound detection, can identify the sound types in the current traffic environment of the Autonomous vehicles, that is, the traffic scene category. The vehicle computer can remind the driver to pay attention to driving safety through the above sound types, so as to improve the safety of the driver and the vehicle.

Medical image analysis is highly susceptible to noise during acquisition and transmission, significantly degrading diagnostic accuracy. This study proposes a robust Attention-Based Squeeze-Excitation Residual Autoencoder (ASE-RAE) for denoising medical images corrupted by Gaussian and Salt-and-Pepper (SP) noise. The network integrates residual skip connections in both encoder and decoder paths, combined with Squeeze-and-Excitation (SE) blocks and a Convolutional Block Attention Module (CBAM) to improve channel and spatial feature representation. The model is benchmarked against traditional denoising techniques, including Non-Local Means Filter (NLMF), Median Filter (MF), Wavelet Transform (WT), Gaussian Filter (GF), and Convolutional Neural Networks (CNNs). Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Mean Squared Error (MSE) demonstrates that the proposed method consistently outperforms all baseline models across varying noise intensities (5%–75%) on the IQ-OTH/NCCD lung CT dataset. Furthermore, ablation studies confirm the contribution of each module (SE and CBAM) to performance gains. The proposed architecture preserves diagnostic features while suppressing both random and structured noise, and its convolutional SE implementation reduces computational overhead compared to conventional fully connected attention blocks. This work highlights a scalable, interpretable solution for image denoising in clinical workflows and sets a foundation for real-time deployment in AI-assisted diagnostics.

The primary focus of this research article is to advance convolutional neural networks (CNNs) through the incorporation of Squeeze and Excitation (SE) and Convolutional Block Attention Module (CBAM) blocks, with an emphasis on recalibrating channel wise features. This approach highlights channel relationships, with the SE block seamlessly integrated into a pre-trained DenseNet CNN model to recalibrate channel wise feature responses by explicitly modeling channel interdependencies. The study demonstrates the construction of SENet and CBAM architectures with SE and CBAM blocks on pretrained DenseNet models using transfer learning, achieving exceptional generalization on a cataract image dataset. The proposed architectures utilize layers of compressed network blocks that can establish direct connections with preceding layers through adjustable weight modifications. Comprehensive evaluations indicate that the DenseSE and DenseCBAM models both achieve 99% testing accuracy and exhibit strong performance across various metrics. Notably, DenseNet169 with SE and CBAM blocks attained 99% precision, surpassing the baseline model’s 95% precision. Furthermore, the Grad CAM technique enhances cataract disease identification, with the DenseCBAM and DenseSE models generating detailed heatmaps that support healthcare professionals in making swift and accurate retinal diagnoses.