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This study presents a deep learning‐based image segmentation approach for leaf disease identification using the U‐Net architecture. Convolutional neural networks (CNNs), particularly U‐Net, are effective for precise segmentation tasks and were trained and validated on a high‐quality “Leaf Disease Segmentation” dataset. Each image contains annotated regions of unhealthy leaf tissue, enabling the model to distinguish between healthy and infected areas. Image preprocessing and augmentation further enhanced model performance and robustness. The U‐Net model, composed of an encoder for context extraction and a decoder for precise segmentation was trained to accurately identify diseased regions at the pixel level. Regularization techniques such as dropout, batch normalization, and ReLU activation were used to prevent overfitting and improve learning. Furthermore, Adam optimizer was employed with a learning rate of 0.001. The model demonstrated strong generalization by accurately segmenting disease regions in unseen validation images. It effectively captured complex patterns in both healthy and diseased leaf sections, outperforming traditional image processing techniques. Trained on 7056 images for 40 epochs, the model achieved 99.70% training accuracy, 0.062 training loss, and 98.99% validation accuracy. These results highlight the model's high accuracy, efficient learning, and robustness, making it suitable for real‐world applications in precision agriculture. This study proposes a deep learning‐based approach for leaf disease identification using the U‐Net architecture for precise segmentation. By training on a dataset of 7056 annotated leaf images, the model effectively distinguishes between healthy and diseased regions, achieving 99.70% training accuracy and 98.99% validation accuracy in 40 epochs. The results demonstrate the model's robustness and potential for real‐world precision agriculture applications.

Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research.

Accurate segmentation of fine roots in field rhizotron imagery is essential for high-throughput root system analysis but remains challenging due to limitations of traditional methods. Traditional methods for root quantification (e.g., soil coring, manual counting) are labor-intensive, subjective, and low-throughput. These limitations are exacerbated in in situ rhizotron imaging, where variable field conditions introduce noise and complex soil backgrounds. To address these challenges, this study develops an advanced deep learning framework for automated segmentation. We propose an improved U-shaped Convolutional Neural Network (U-Net) architecture optimized for segmenting larch (Larix olgensis) fine roots under heterogeneous field conditions, integrating both in situ rhizotron imagery and open-source multi-species minirhizotron datasets. Our approach integrates (1) a Convolutional Block Attention Module (CBAM) to enhance feature representation for fine-root detection; (2) an additive feature fusion strategy (UpAdd) during decoding to preserve morphological details, particularly in low-contrast regions; and (3) a transfer learning protocol to enable robust cross-species generalization. Our model achieves state-of-the-art performance with a mean intersection over union (mIoU) of 70.18%, mean Recall of 86.72%, and mean Precision of 75.89%—significantly outperforming PSPNet, SegNet, and DeepLabV3+ by 13.61%, 13.96%, and 13.27% in mIoU, respectively. Transfer learning further elevates root-specific metrics, yielding absolute gains of +0.47% IoU, +0.59% Precision, and +0.35% F1-score. The improved U-Net segmentation demonstrated strong agreement with the manual method for quantifying fine-root length, particularly for third-order roots, though optimization of lower-order root identification is required to enhance overall accuracy. This work provides a scalable approach for advancing automated root phenotyping and belowground ecological research.

The efficiency of photovoltaic (PV) systems is often compromised by undetected faults, exacerbated by the complexity of thermal imagery backgrounds. This study presents a novel deep-learning-based approach to enhance fault detection in PV systems by customizing the Atrous Spatial Pyramid Pooling (ASPP) module within a U-Net architecture. We propose and evaluate three modified configurations U-Net-ASPP_Cent, U-Net-ASPP_Diag, and U-Net-ASPP_Hybrid each designed to address specific fault localization challenges, including central and diagonal fault patterns. These configurations aim to overcome the limitations of conventional U-Net-ASPP by enhancing multiscale feature extraction and improving segmentation accuracy in complex PV thermal images. The U-Net-ASPP_Hybrid configuration demonstrated the most balanced performance across all key metrics, achieving a 1.13% improvement in F1-score, a 3.01% increase in Intersection over Union (IoU), and a 9.86% reduction in loss compared to the baseline U-Net-ASPP. Additionally, the U-Net-ASPP_Cent and U-Net-ASPP_Diag configurations provided IoU gains of 1.18% and 1.96%, respectively, while also reducing false positive rates. These results highlight the effectiveness of incorporating region-specific dilation strategies, enhancing the model’s ability to detect diverse and challenging fault patterns in complex thermal imagery. Beyond quantitative performance, qualitative segmentation analysis confirms that the U-Net-ASPP_Hybrid model offers superior fault localization and adaptability to real-world PV inspections. The U-Net-ASPP_Cent model is particularly effective for central anomaly detection, while the U-Net-ASPP_Diag model excels at identifying directional faults such as cracks. The U-Net-ASPP_Hybrid model, combining both strategies, provides a comprehensive solution for automated PV fault detection. These findings underscore the stability, scalability, and real-world applicability of the proposed models, making them ideal for automated PV inspection systems aimed at minimizing manual intervention and enhancing the reliability of renewable energy infrastructure. Future research will explore adaptive dilation strategies and more diverse datasets to further improve model generalization across varying PV environments.

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond. •MGA-Net enhances U-net for precise neonatal brain extraction and reconstruction.•Uses a common encoder, dual decoders to generate masks, and reconstruct images.•Sinusoidal positional encoding handles MRI and ultrasound images.•Outperforms traditional methods in brain extraction, and reconstruction.•Extensive validation shows improved accuracy, recall, and structural similarity.

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0–2 h. Existing methods use radar echo maps and the Z–R relationship to directly predict future rainfall rates through deep learning methods, which are not physically constrained, but suffer from severe loss of predicted image details. This paper proposes a new model framework to effectively solve this problem, namely LSTMAtU-Net. It is based on the U-Net architecture, equipped with a Convolutional LSTM (ConvLSTM) unit with the vertical flow direction and depthwise-separable convolution, and we propose a new component, the Efficient Channel and Space Attention (ECSA) module. The ConvLSTM unit with the vertical flow direction memorizes temporal changes by extracting features from different levels of the convolutional layers, while the ECSA module innovatively integrates different structural information of each layer of U-Net into the channelwise attention mechanism to learn channel and spatial information, thereby enhancing attention to the details of precipitation images. The experimental results showed that the performance of the model on the test dataset was better than other examined models and improved the accuracy of medium- and high-intensity precipitation nowcasting.

Skin cancer is a frequent cancer globally, and the outlook for a patient and the effectiveness of treatment depend on its prompt identification. Dermoscopic images are very essential in the accurate and automatic segmentation of skin lesions for helping clinicians diagnose skin cancer. We, in this study, propose a new semantic segmentation model based on the DoubleU-Net architecture for improving the detail and accuracy of skin lesion detection. The proposed DoubleU-Net model works by integrating two U-Net networks in sequence, where the first U-Net extracts high-level features and provides an initial segmentation map. The second U-Net refines this output by learning from the residual errors of the first network and produces a more detailed and accurate segmentation. This dual network design helps in overcoming the challenges of blurred lesion boundaries and varying lesion sizes, which are common issues in skin lesion segmentation. We evaluated the performance of our model using the publicly available ISIC(2018) dataset which contains thousands of annotated dermoscopic images. Our model evolved with the Dice coefficient and losing cross-entropy in order to deal with class imbalance, which is frequently observed in medical datasets. Experimental results show that our proposed DoubleU-Net architecture performs more effectively than baseline U-Net model when using the metrics Intersection over Union (0.81589), Dice coefficient (0.88628), and overall segmentation accuracy (0.94437).

Alzheimer’s disease (AD) is an irreversible, progressive, and ultimately fatal brain degenerative disorder, no effective cures for it till now. Despite that, the available treatments can delay its progress. So, early detection of AD plays a crucial role in preventing and controlling its progress. Hippocampus (HC) is among the first impacted brain regions by AD. Its shape and volume are measured using a structural magnetic resonance image (MRI) to help AD diagnosis. Therefore, brain hippocampus segmentation is the building block for AD detection. This study’s main objective is to propose a deep learning Alzheimer’s disease hippocampus segmentation framework (DL-AHS) for automatic left and right hippocampus segmentation to detect and identify AD. The proposed DL-AHS framework is based on the U-Net architecture and estimated on the baseline coronal T1-weighted structural MRI data obtained from Alzheimer’s disease neuroimaging initiative (ADNI) and neuroimaging tools and resources collaboratory (NITRIC) datasets. The dataset is processed using the Medical Image Processing, Analysis, and Visualization (MIPAV) program. Besides, it is augmented using a deep convolutional generative adversarial network (DC-GAN). For left and right HC segmentation from other brain sub-regions, two architectures are proposed. The first utilizes simple hyperparameters tuning in the U-Net (SHPT-Net). The second employs a transfer learning technique in which the ResNet blocks are used in the U-Net (RESU-Net). The empirical results confirmed that the proposed framework achieves high performance, 94.34% accuracy, and 93.5% Dice similarity coefficient for SHPT-Net. Also, 97% accuracy and 94% Dice similarity coefficient are achieved for RESU-Net.

This work introduces the MSFAUMobileNet model, a complex U-Net structure tailored for retinal blood vessel segmentation, which is a critical process for detecting and monitoring retinal diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration (AMD). The model uses Multi-Scale Feature Aggregation (MSFA), Residual Connections, and Attention Mechanisms to enhance its segmentation accuracy. Utilizing MobileNetV2 as the encoder, the model is capable of effectively generating 13 bottleneck layers’ worth of hierarchical features. Although residual connections and attention mechanisms are useful in improving the segmentation process and guaranteeing the precise outlining of intricate vascular networks, MSFA extracts spatial information at various resolutions. The model was tested on the DRIVE dataset and produced exceptionally high scores with accuracy at 99.99%, Dice coefficient at 99.95%, and Intersection over Union (IoU) at 99.94%. These scores show how efficiently the model separates the complex retinal network, enabling early treatment and detection of retinal disease. MSFAUMobileNet is a good medical image analysis software for real clinical practice owing to its computational speed and precision, particularly in the management of retinal disease.