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graphics processing unit (gpu) accelerated filtered back-projection (fbp) algorithm  (MathWorks Inc)


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    MathWorks Inc graphics processing unit (gpu) accelerated filtered back-projection (fbp) algorithm
    Graphics Processing Unit (Gpu) Accelerated Filtered Back Projection (Fbp) Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/fbp+algorithm/pmc09990133-128-8-16
    Average 90 stars, based on 1 article reviews
    graphics processing unit (gpu) accelerated filtered back-projection (fbp) algorithm - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: A Proposed MetaNet Deep Learning for Medical Diseases Classification
    Article Snippet: The Metaheuristic Gray Wolf Optimization (GWO) algorithm, based on Swarm Optimization, is widely used in various fields, including machine learning and numerical optimization.. However, extracting meaningful features and useful content from large collections of medical images remains challenging for computer vision applications, necessitating improved feature classification accuracy.. This paper proposes a hybrid Deep Neural Network (DNN) and GWO approach, termed MetaNet, for medical image classification.

    Article Title: Nanoporous Submicron Gold Particles Enable Nanoparticle-Based Localization Optoacoustic Tomography (nanoLOT).
    Article Snippet: See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense was reconstructed using a self-developed GPU-based back projection algorithm using Matlab 2022b (Mathworks).

    Article Title: Treatment of Nocturnal Enuresis Using Miniaturised Smart Mechatronics With Artificial Intelligence
    Article Snippet: A computing device with a GPU ( ) supported by the Parallel Computing Toolbox of Matlab is used to mitigate the very long training process and computational burden of training in Bi-LSTM-RNN.

    Software:

    Article Title: A novel stacked generalization of models for improved TB detection in chest radiographs
    Article Snippet: .. For this study, we used a Windows ® system with Intel ® Xeon ® CPU E5-2640v3 2.60-GHz processor, 1 TB of Hard Disk space, 16 GB RAM, a CUDA-enabled Nvidia GTX 1080 Ti 11GB graphical processing unit (GPU), Matlab R2017b, Weka 3 ML software, and CUDA 8.0/cuDNN 5.1 dependencies for GPU acceleration. ..



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