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resnet50 network  (Xilinx Inc)

 
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    Xilinx Inc resnet50 network
    Resnet50 Network, supplied by Xilinx 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/resnet50+network/resnet50/pm40593288-195-7-12
    Average 90 stars, based on 1 article reviews
    resnet50 network - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI.
    Article Snippet: The approximate resource consumption values when using ResNet50 network on PL in Xilinx KV260 Kria are given in the Table 3.



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    ResNet50 architecture (including residual learning block).

    Journal: Sensors (Basel, Switzerland)

    Article Title: Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering

    doi: 10.3390/s24061725

    Figure Lengend Snippet: ResNet50 architecture (including residual learning block).

    Article Snippet: The key findings and conclusions are summarized as follows: ResNet50 neural network algorithm: A ResNet50 neural network algorithm for concrete crack detection was developed and implemented in Matlab R2020b.

    Techniques: Blocking Assay

    Limitations of the state-of-the-art-work on hypertensive retinopathy.

    Journal: Diagnostics

    Article Title: Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture

    doi: 10.3390/diagnostics13081439

    Figure Lengend Snippet: Limitations of the state-of-the-art-work on hypertensive retinopathy.

    Article Snippet: Hacisoftaoglu et al. [ ] , This study uses a DL approach and the ResNet50 network to develop an autonomous detection model for smartphone-based retinal images. , EyePACS = 35,126 Images, Messidor = 1187 Images, Messidor-2 = 1748 Images , ACC = 91% SEN = 92% SPE = 90% , Only a few publicly available datasets were used to train and test the proposed model..

    Techniques: Biomarker Discovery