Showing 801 - 820 results of 2,333 for search 'blocking detection', query time: 0.11s Refine Results
  1. 801

    Temporal Effect on PD‐L1 Detection and Novel Insights Into Its Clinical Implications in Non–Small Cell Lung Cancer by Gopal P. Pathak, Rashmi Shah, Mathieu Castonguay, Angela Cheng, John Fris, Rowan Murphy, Gail Darling, Alexander Ednie, Daniel French, Harry Henteleff, Aneil Mujoomdar, Madelaine Plourde, Alison Wallace, Zhaolin Xu

    Published 2024-10-01
    “…Furthermore, the presence of a KRAS mutation favored the outcome of anti‐PD‐L1/PD1 immunotherapy in advanced NSCLC. Conclusion PD‐L1 detection from tissue blocks was found to vary temporally, urging for a prioritized consideration for patients with marginal scores when archived blocks are employed for its detection. …”
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  2. 802

    CDSE-UNet: Enhancing COVID-19 CT Image Segmentation With Canny Edge Detection and Dual-Path SENet Feature Fusion by Jiao Ding, Jie Chang, Renrui Han, Li Yang

    Published 2025-01-01
    “…In response to blurred boundaries and high variability characteristic of lesion areas in COVID-19 CT images, we introduce CDSE-UNet: a novel UNet-based segmentation model that integrates Canny operator edge detection and a Dual-Path SENet Feature Fusion Block (DSBlock). …”
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  3. 803

    Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models by Kaoutar Clero, Said Ed-Diny, Mohammed Achalhi, Mouhamed Cherkaoui, Imad El Harraki, Sanaa El Fkihi, Intissar Benzakour, Tarik Soror, Said Rziki, Hamd Ait Abdelali, Hicham Tagemouati, François Bourzeix

    Published 2025-06-01
    “…Rockfalls are among the frequent hazards in underground mines worldwide, requiring effective methods for detecting unstable rock blocks to ensure miners' and equipment's safety. …”
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  4. 804

    MS-trust: a transformer model with causal-global dual attention for enhanced MRI-based multiple sclerosis and myelitis detection by Salha M. Alzahrani

    Published 2025-06-01
    “…This paper presents MS-Trust that features a causality attention block to maintain the sequential integrity of MRI data and a global attention block to capture long-range dependencies and global contextual information, with squeeze-and-excitation block to recalibrate channel-wise feature responses. …”
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  5. 805
  6. 806

    MicrocrackAttentionNext: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks Through Feature Visualization by Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar, Frank Wuttke, Sven Tomforde

    Published 2025-03-01
    “…This study proposes an asymmetric encoder–decoder network with an adaptive feature reuse block for microcrack detection. The impact of various activation and loss functions are examined through feature space visualisation using the manifold discovery and analysis (MDA) algorithm. …”
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  7. 807
  8. 808

    A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 by Burhan Duman

    Published 2025-01-01
    “…Industry requires defect detection to ensure the quality and safety of products. …”
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  9. 809

    Binocular Video-Based Automatic Pixel-Level Crack Detection and Quantification Using Deep Convolutional Neural Networks for Concrete Structures by Liqu Liu, Bo Shen, Shuchen Huang, Runlin Liu, Weizhang Liao, Bin Wang, Shuo Diao

    Published 2025-01-01
    “…Crack detection and quantification play crucial roles in assessing the condition of concrete structures. …”
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  10. 810

    DeployFusion: A Deployable Monocular 3D Object Detection with Multi-Sensor Information Fusion in BEV for Edge Devices by Fei Huang, Shengshu Liu, Guangqian Zhang, Bingsen Hao, Yangkai Xiang, Kun Yuan

    Published 2024-10-01
    “…To address the challenges of suboptimal remote detection and significant computational burden in existing multi-sensor information fusion 3D object detection methods, a novel approach based on Bird’s-Eye View (BEV) is proposed. …”
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  11. 811

    Detection of Bacterial Leaf Spot Disease in Sesame (<i>Sesamum indicum</i> L.) Using a U-Net Autoencoder by Minju Lee, Jeseok Lee, Amit Ghimire, Yegyeong Bae, Tae-An Kang, Youngnam Yoon, In-Jung Lee, Choon-Wook Park, Byungwon Kim, Yoonha Kim

    Published 2025-06-01
    “…This supports the potential of this wavelength for the early-stage detection of bacterial leaf spots in sesame.…”
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  12. 812

    A Novel Spatter Detection Algorithm for Real-Time Quality Control in Laser-Directed Energy Deposition-Based Additive Manufacturing by Farzaneh Kaji, Jinoop Arackal Narayanan, Mark Zimny, Ehsan Toyserkani

    Published 2025-06-01
    “…Furthermore, spatter detection is employed to assess the impact of spatter formation on deposition continuity. …”
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  13. 813

    An online 11 kv distribution system insulator defect detection approach with modified YOLOv11 and mobileNetV3 by Arnav Bhagwat, Soham Dutta, Debdeep Saha, Maddikara Jaya Bharata Reddy

    Published 2025-05-01
    “…Additionally, multiple case studies were conducted to validate the method’s robustness and reliability for insulator defect detection. This paper incorporates a modified version of YOLOv11 architecture using the constituent C3K2, SPFF and C2PSA algorithmic blocks, mounted with a MobileNetV3 classifier to allow lightweight framework in DAS based devices. …”
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  14. 814

    Hybrid Na&#x00EF;ve Bayes Models for Scam Detection: Comparative Insights From Email and Financial Fraud by Lebede Ngartera, Mahamat Ali Issaka, Saralees Nadarajah

    Published 2025-01-01
    “…This study revisits the Na&#x00EF;ve Bayes algorithm&#x2014;often underestimated in modern cybersecurity&#x2014;as a core building block for effective scam detection. By looking at two specific case studies on phishing emails and financial fraud detection, we look into the math behind Na&#x00EF;ve Bayes and talk about its flaws, such as the assumption that features are independent, problems with high-dimensional data, and extreme class imbalance. …”
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  15. 815

    Kidney Stone Detection based on Improved YOLOv7 with Attention Module and Super Resolution Techniques Under Limited Training Samples by Minh Tai Pham Nguyen, Viet Tuan Le, Huu Thanh Duong, Vinh Truong Hoang

    Published 2025-08-01
    “…As a result, the proposed YOLOv7 with attention modules easily outperforms the YOLOv7 baseline in detection performance, the highest accuracy model belongs to convolution block attention module attached with YOLOv7, which reaches 91.2% mAP50. …”
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  16. 816
  17. 817

    Real-Time Polyp Detection From Endoscopic Images Using YOLOv8 With YOLO-Score Metrics for Enhanced Suitability Assessment by Zahid Farooq Khan, Muhammad Ramzan, Mudassar Raza, Muhammad Attique Khan, Areej Alasiry, Mehrez Marzougui, Jungpil Shin

    Published 2024-01-01
    “…Early diagnosis is based on the detection and analysis of polyps which are cancer precursors linked to aging and declining health. …”
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  18. 818

    JointNet4BCD: A Semi-Supervised Joint Learning Neural Network with Decision Fusion for Building Change Detection by Hao Chen, Chengzhe Sun, Jun Li, Chun Du

    Published 2024-12-01
    “…Furthermore, to improve the semantic understanding capability of the model, we propose a joint learning approach for building extraction and change detection tasks. Lastly, a decision fusion block is designed to fuse the building extraction results into the building change detection results to further improve the accuracy of building change detection. …”
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  19. 819

    FGS-YOLOv8s-seg: A Lightweight and Efficient Instance Segmentation Model for Detecting Tomato Maturity Levels in Greenhouse Environments by Dongfang Song, Ping Liu, Yanjun Zhu, Tianyuan Li, Kun Zhang

    Published 2025-07-01
    “…This study proposes an improved instance segmentation model called FGS-YOLOv8s-seg, which achieves accurate detection and maturity grading of tomatoes in greenhouse environments. …”
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  20. 820

    Accurate Sugarcane Detection and Row Fitting Using SugarRow-YOLO and Clustering-Based Spline Methods for Autonomous Agricultural Operations by Guiqing Deng, Fangyue Zhou, Huan Dong, Zhihao Xu, Yanzhou Li

    Published 2025-07-01
    “…The method aims to achieve accurate sugarcane identification and provide basic support for subsequent sugarcane row detection. This model introduces the WTConv convolutional modules to expand the sensory field and improve computational efficiency, adopts the iRMB inverted residual block attention mechanism to enhance the modeling capability of crop spatial structure, and uses the UIOU loss function to effectively mitigate the misdetection and omission problem in the region of dense and overlapping targets. …”
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