A Multiscale Attention Segment Network-Based Semantic Segmentation Model for Landslide Remote Sensing Images

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(10), P. 1712 - 1712

Published: May 11, 2024

Landslide disasters have garnered significant attention due to their extensive devastating impact, leading a growing emphasis on the prompt and precise identification detection of landslides as prominent area research. Previous research has primarily relied human–computer interactions visual interpretation from remote sensing identify landslides. However, these methods are time-consuming, labor-intensive, subjective, low level accuracy in extracting data. An essential task deep learning, semantic segmentation, been crucial automated image recognition tasks because its end-to-end pixel-level classification capability. In this study, mitigate disadvantages existing landslide methods, we propose multiscale segment network (MsASNet) that acquires different scales features, designs an encoder–decoder structure strengthen boundary, combines channel mechanism feature extraction The MsASNet model exhibited average 95.13% test set Bijie’s dataset, mean 91.45% Chongqing’s 90.17% Tianshui‘s signifying ability extract information efficiently accurately real time. Our proposed may be used efforts toward prevention control geological disasters.

Language: Английский

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A Review on Medical Image Segmentation: Datasets, Technical Models, Challenges and Solutions

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 20, 2025

ABSTRACT Medical image segmentation is prerequisite in computer‐aided diagnosis. As the field experiences tremendous paradigm changes since introduction of foundation models, technicality deep medical model no longer a privilege limited to computer science researchers. A comprehensive educational resource suitable for researchers broad, different backgrounds such as biomedical and medicine, needed. This review strategically covers evolving trends that happens fundamental components emerging multimodal datasets, updates on learning libraries, classical‐to‐contemporary development models latest challenges with focus enhancing interpretability generalizability model. Last, conclusion section highlights future worth further attention investigations.

Language: Английский

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LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation

Bioengineering, Journal Year: 2024, Volume and Issue: 11(6), P. 545 - 545

Published: May 27, 2024

Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, proposed in this paper. This network attempts model spatial dependencies while maintaining performance, better distinguish background noise thus improve accuracy. A Fusion Attention Encoder (FAEncoder) designed which integrates Large Kernel (LKA) channel attention mechanisms extract deep representational features unearth dependencies. Furthermore, newly Visual Mamba module (VAM) added skip connections, modeling encoder-extracted reducing interference through mechanism. Finally, Pyramid Split (PSA) used bottleneck layer richer multi-scale contextual features. The method was thoroughly evaluated four renowned datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, ETIS. Experimental findings demonstrate that delivers higher accuracy less time, consistently outperforming most advanced networks.

Language: Английский

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GAN-Driven Liver Tumor Segmentation: Enhancing Accuracy in Biomedical Imaging

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(5)

Published: June 13, 2024

Language: Английский

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An Edge-Enhanced Network for Polyp Segmentation

Bioengineering, Journal Year: 2024, Volume and Issue: 11(10), P. 959 - 959

Published: Sept. 25, 2024

Colorectal cancer remains a leading cause of cancer-related deaths worldwide, with early detection and removal polyps being critical in preventing disease progression. Automated polyp segmentation, particularly colonoscopy images, is challenging task due to the variability appearance low contrast between surrounding tissues. In this work, we propose an edge-enhanced network (EENet) designed address these challenges by integrating two novel modules: covariance attention (CEEA) cross-scale edge enhancement (CSEE) modules. The CEEA module leverages covariance-based enhance boundary detection, while CSEE bridges multi-scale features preserve fine-grained details. To further improve accuracy introduce hybrid loss function that combines cross-entropy edge-aware loss. Extensive experiments show EENet achieves Dice score 0.9208 IoU 0.8664 on Kvasir-SEG dataset, surpassing state-of-the-art models such as Polyp-PVT PraNet. Furthermore, it records 0.9316 0.8817 CVC-ClinicDB demonstrating its strong potential for clinical application segmentation. Ablation studies validate contribution

Language: Английский

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A comprehensive survey of semantic segmentation based on computer vision

Published: Jan. 9, 2025

Semantic segmentation is a significant and demanding work in computer vision it has gained more attention worldwide. This article delivers an in-depth analysis of vision-based semantic approaches for 3D point cloud data. investigates the emergence development both domestically internationally. It also outlines historical evolution various branches emphasizing recent advancements driven by deep learning techniques. Despite notable progress, challenges persist, including handling variability object shapes sizes, computational costs, robustness against different conditions. survey aims to evaluate synthesize current research, identifying strengths weaknesses traditional modern methods, highlighting potential future research directions. The study offers valuable information on implementation performance presenting comprehensive methodologies, datasets, evaluation metrics guiding researchers towards suitable techniques several applications.

Language: Английский

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MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation

Algorithms, Journal Year: 2025, Volume and Issue: 18(1), P. 42 - 42

Published: Jan. 12, 2025

The colonoscopy procedure heavily relies on the operator’s expertise, underscoring importance of automated polyp segmentation techniques in enhancing efficiency and accuracy colorectal cancer diagnosis. Nevertheless, achieving precise remains a significant challenge due to high visual similarity between polyps their backgrounds, blurred boundaries, complex localization. To address these challenges, Multi-scale Selective Edge-Aware Network has been proposed facilitate segmentation. model consists three key components: (1) an Edge Feature Extractor (EFE) that captures edge features with precision during initial encoding phase, (2) Cross-layer Context Fusion (CCF) block designed extract integrate multi-scale contextual information from diverse receptive fields, (3) Aware (SEA) module enhances sensitivity high-frequency details decoding thereby improving preservation accuracy. effectiveness our rigorously validated Kvasir-SEG, Kvasir-Sessile, BKAI datasets, mean Dice scores 91.92%, 82.10%, 92.24%, respectively, test sets.

Language: Английский

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A multi-stage deep learning network toward multi-classification of polyps in colorectal images

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 119, P. 189 - 200

Published: Feb. 5, 2025

Language: Английский

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Adapting Classification Neural Network Architectures for Medical Image Segmentation Using Explainable AI

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(2), P. 55 - 55

Published: Feb. 13, 2025

Segmentation neural networks are widely used in medical imaging to identify anomalies that may impact patient health. Despite their effectiveness, these face significant challenges, including the need for extensive annotated data, time-consuming manual segmentation processes and restricted data access due privacy concerns. In contrast, classification networks, similar capture essential parameters identifying objects during training. This paper leverages this characteristic, combined with explainable artificial intelligence (XAI) techniques, address challenges of segmentation. By adapting tasks, proposed approach reduces dependency on To demonstrate concept, Medical Decathlon 'Brain Tumours' dataset was utilised. A ResNet network trained, XAI tools were applied generate segmentation-like outputs. Our findings reveal GuidedBackprop is among most efficient effective methods, producing heatmaps closely resemble masks by accurately highlighting entirety target object.

Language: Английский

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Highlighting the Advanced Capabilities and the Computational Efficiency of DeepLabV3+ in Medical Image Segmentation: An Ablation Study

BioMedInformatics, Journal Year: 2025, Volume and Issue: 5(1), P. 10 - 10

Published: Feb. 14, 2025

Background: In clinical practice, identifying the location and extent of tumors lesions is crucial for disease diagnosis treatment. Artificial intelligence, particularly deep neural networks, offers precise automated segmentation, yet limited data high computational demands often hinder its application. Transfer learning helps mitigate these challenges by significantly reducing costs, although applying models can still be resource intensive. This study aims to present flexible computationally efficient architecture that leverages transfer delivers highly accurate results across various medical imaging problems. Methods: We evaluated three datasets with varying similarities ImageNet: ISIC 2018 (skin lesions), CBIS-DDSM (breast masses), Shenzhen Montgomery CXR Set (lung segmentation). An ablation on tested pre-trained backbones, architectures, loss functions. Results: The optimal configuration—DeepLabV3+ a ResNet50 backbone Log-Cosh Dice loss—was validated remaining datasets, achieving state-of-the-art results. Conclusion: Computationally simpler architectures deliver robust performance without extensive resources, establishing DeepLabV3+ as baseline future studies. domain, enhancing quality more critical improving segmentation accuracy than increasing model complexity.

Language: Английский

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