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Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. To achieve the the purpose of identifying burned area accurately and efficiency from remote sensing images, a lightweight deep learning model is proposed based on Deeplab V3 + , which employs the combination of attention mechanism and deep transitive transfer learning (DTTL) strategy. The lightweight MobileNet V2 network integrated with Convolutional Block Attention Module (CBAM) is designed as the backbone network to replace the traditional time-consuming Xception of Deeplab V3 +. The attention mechanism is introduced to enhance the recognition ability of the proposed deep learning model, and the deep transitive transfer learning strategy is adopted to solve the problem of incorrect identification of the burned area and discontinuous edge details caused by insufficient sample size during the extraction process. For the process of DTTL, the improved Deeplab V3 + network was first pre-trained on ImageNet. Sequentially, WorldView-2 and the Sentinel-2 dataset were employed to train the proposed network based on the ImageNet pre-trained weights. Experiments were conducted to extract burned area from remote sensing images based on the trained model, and the results show that the proposed methodology can improve extraction accuracy with OA of 92.97% and Kappa of 0.819, which is higher than the comparative methods, and it can reduce the training time at the same time. We applied this methodology to identify the burned area in Western Attica region of Greece, and a satisfactory result was achieved with. OA of 93.58% and Kappa of 0.8265. This study demonstrates the effectiveness of the improved Deeplab V3 +in identifying forest burned area. which can provide valuable information for forest protection and monitoring.

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.

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.

Compared with traditional manual inspection, inspection robots can not only meet the all-weather, real-time, and accurate inspection needs of substation inspection, they also reduce the work intensity of operation and maintenance personnel and decrease the probability of safety accidents. For the urgent demand of substation inspection robot intelligence enhancement, an environment understanding algorithm is proposed in this paper, which is an improved DeepLab V3+ neural network. The improved neural network replaces the original dilate rate combination in the ASPP (atrous spatial pyramid pooling) module with a new dilate rate combination with better segmentation accuracy of object edges and adds a CBAM (convolutional block attention module) in the two up-samplings, respectively. In order to be transplanted to the embedded platform with limited computing resources, the improved neural network is compressed. Multiple sets of comparative experiments on the standard dataset PASCAL VOC 2012 and the substation dataset have been made. Experimental results show that, compared with the DeepLab V3+, the improved DeepLab V3+ has a mean intersection-over-union (mIoU) of eight categories of 57.65% on the substation dataset, with an improvement of 6.39%, and the model size of 13.9 M, with a decrease of 147.1 M.

With the continuous development of deep learning technologies, fault prediction techniques based on various neural networks have been evolving. The deep learning modules based on U-Net residual networks have shown significant advantages in both learning efficiency and effectiveness. In this paper, we propose a deep learning model that integrates a 3D U-Net residual architecture, Convolutional Block Attention Module (CBAM), and Multi-scale Enhanced Global Attention (MEGA) module for automatic seismic fault detection and segmentation. This model can effectively handle complex 3D seismic data, fully exploiting both spatial and channel information, significantly improving the prediction accuracy for small faults, while only slightly increasing the computational cost.Firstly, the model uses the 3D U-Net as the backbone framework, where the residual blocks (BasicRes) extract features through multiple convolution layers. The CBAM module is incorporated to apply attention weighting, enhancing the model's ability to focus on critical information. The CBAM module combines channel attention and spatial attention, effectively adjusting the importance of feature maps from different dimensions, enabling the model to identify potential fault features in complex seismic data.Secondly, the MEGA module is introduced into the model, which further improves the model's feature representation ability by fusing multi-scale features and applying a global attention mechanism. By weighting global information, the MEGA module helps the model better capture key seismic fault features during feature fusion. This design allows the model to focus not only on local details but also to fully utilize the global contextual information in 3D data, thereby enhancing the accuracy of fault detection.After validation, the model achieved promising results in seismic fault detection tasks, automatically identifying and segmenting fault structures in seismic data. The accuracy was improved from 80% with the original 3D U-Net residual network to 85%-87%. This provides strong support for applications such as seismic exploration and subsurface imaging.Keywords: Seismic Fault Detection, 3D U-Net, Convolutional Block Attention Module (CBAM), Multi-scale Enhanced Global Attention (MEGA), Deep Learning

MC-ShuffleNetV2: A lightweight model for maize disease recognition

Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.

Motivated by the increasing popularity and success of attention mechanisms, we observe that popular convolutional attention models like Squeeze-and-Excite (SE) and Convolutional Block Attention Module (CBAM) rely on expensive multi-layer perceptron (MLP) layers. These MLP layers significantly increase computational complexity, making such models less applicable to 3D image contexts, where the data dimensionality and computational costs are inherently higher. In 3D medical imaging, such as pulmonary CT scans, efficient processing is crucial due to the large volume of data. Traditional 2D attention mechanisms, when generalized to 3D, increase the computational load, creating a demand for more efficient attention mechanisms that can operate effectively within a 3D tasks. In this work, we investigate the possibility of incorporating fully convolutional attention within 3D context. Particularly, we present two 3D fully convolutional attention blocks in which we demonstrate effectiveness within the 3D context. We demonstrate effectiveness of the proposed method using the pulmonary CT scans for 3D lung nodule detection. Building on existing 3D nodule detection methods, we present AttentNet, an automated lung nodule detection framework from CT images that performs detection as an ensemble of two stages, candidate proposal stage (Region Proposal Network), in which a high number of candidates is produced, and a false positive reduction stage to reduce the number of false alarms. Throughout our work, we compare the proposed 3D convolutional attention blocks to a number of popular 2D convolutional attention mechanisms by generalizing them to 3D modules, including SE units and CBAM channel attention and spatial attention units. We also compare these to Transformer self-attention units. For the False positive reduction stage, we incorporate a joint analysis approach that considers the variable nodule morphology by aggregating spatial information from different contextual levels. We use LUNA-16 lung nodule detection dataset to demonstrate the benefits of using the proposed fully convolutional attention blocks comparing to baseline popular lung nodule detection methods where no attention is used. It is worth noting that our work does not aim at achieving state-of-the-art results in the lung nodule detection task, rather to demonstrate the benefits of incorporating fully convolutional attention within a 3D context.

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.

Early diagnosis of eye diseases plays a critical role in treatment success and public health. With the widespread use of modern medical imaging methods, the development of automated diagnostic systems from retinal fundus images has become an important research area. In this study, the effects of integrating the Convolutional Block Attention Module (CBAM) into EfficientNetB0 and DenseNet121 architectures were investigated for the classification of cataract, diabetic retinopathy, glaucoma, and healthy subjects. Experimental results demonstrated that the CBAM attention mechanism enhances accuracy and generalization performance, particularly in distinguishing complex retinal findings. For DenseNet121, accuracy, precision, recall, and F1-score were obtained as 88.37%, 89.66%, 88.37%, and 88.52%, respectively. EfficientNetB0 achieved 96.32% accuracy, 96.34% precision, 96.32% recall, and 96.33% F1-score. After CBAM integration, the accuracy of DenseNet121 increased to 90.39% and its F1-score to 90.54%, while EfficientNetB0 improved to 96.56% accuracy and 96.57% F1-score. These results reveal that the incorporation of CBAM enhances the performance of deep learning models and significantly contributes to the development of reliable and clinically applicable systems for the automated detection of eye diseases

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM_SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)'s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM_SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset.

Artificial Intelligence (AI) based ultrasound image processing is quite important for diagnosis of renal tumors. Traditional networks, however, struggle with accurate lesion segmentation and differentiation between benign and malignant cases in complex ultrasound images. This paper introduces an advanced multitask network model to overcome these limitations. Our model combines a deep separable Convolutional ResNet with a NestedUNet, enriched with a Convolutional Block Attention Module (CBAM) and residual connections, enhancing its precision in lesion segmentation and classification in intricate images. Compared with the existing methods, the model proposed in this study has the following innovative contributions: (1) combining depthwise separable convolution and NestedUNet, the computational efficiency and feature extraction ability of the model are significantly improved; (2) The Convolutional Block Attention Module (CBAM) is introduced to enhance the attention to key features, to effectively distinguish lesions in complex backgrounds; (3) The gradient flow is improved by the residual connection, which ensures the stable training of the deep network. Experimental results show that the IoU of the model on the kidney tumor dataset reaches 94.23%, which is significantly better than that of STUNet (92.31%). These results show that the model proposed in this study has significant advantages in processing complex ultrasound images, providing a more accurate basis for clinical diagnosis, and has a high application potential.

Modulation recognition of single‐component radar signals has been widely studied in the fewer years. Meanwhile, as the electromagnetic environment in the actual battlefields is ever‐increasingly complicated, the demand for the modulation recognition of multi‐component radar signals is also increasing. However, the modulation recognition of single‐component radar signals performs poorly on multi‐component radar signals. To address this problem, a novel modulation recognition method of multi‐component radar signals based on the deep convolutional neural network with a convolutional block attention module (DCNN‐CBAM) is proposed, and the network architecture mainly includes mobile‐inverted bottleneck convolution (MBConv), Fused‐MBConv, and convolutional block attention module (CBAM). An adaptive optimization algorithm Adam with weighted gradient and dynamic bound is proposed for accelerating convergence speed and generalisation performance. The proposed method can not only recognise dual‐component and three‐component radar signals but also four‐component radar signals. The experimental results demonstrate that the recognition accuracies of all radar signals modulation types are more than 83.57% at −8 dB for dual‐component radar signals, 67.41% for three‐component radar signals, and 57.95% for four‐component radar signals, which verify the better recognition performance of DCNN‐CBAM for multi‐component radar signals. The recognition accuracy of the proposed DCNN‐CBAM for dual‐component radar signals can be up to 95.95% at −6 dB, which shows superior recognition performance over the residual method. This work provides an essential guidance for enhancing the recognition performance of multi‐component radar signals.

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.

Change detection with SAR images encounters more difficulties than optical ones due to the presence of the speckle noise. Recently, inspired by the human visual systems, self-attention mechanism is incorporated in large-scale classification tasks to improve the classification performance. Inspired by this, this paper proposed a novel change detection method from SAR images based on self-attention mechanism. First, training samples with pseudo-labels are generated by the log-ratio operator and hierarchical FCM algorithm. Image patches around these pixel are generated, and these patches are fed into a network with the convolutional block attention module (CBAM). After training, all the pixels from multitemporal SAR images are fed into the learned network, and then the final change map can be obtained. Experimental results on two real SAR datasets demonstrate the superiority of the proposed method over two closely related methods.