Search alternatives:
segmentation algorithm » selection algorithm (Expand Search)
feature segmentation » feature representation (Expand Search), feature selection (Expand Search), image segmentation (Expand Search)
based optimization » whale optimization (Expand Search)
texture feature » texture features (Expand Search)
binary texture » binary mixture (Expand Search), binary mixtures (Expand Search), binary measure (Expand Search)
binary wave » binary image (Expand Search)
wave based » made based (Expand Search), game based (Expand Search), rate based (Expand Search)
Showing 1 - 11 results of 11 for search '(( binary texture feature segmentation algorithm ) OR ( binary wave based optimization algorithm ))', query time: 0.98s Refine Results
  1. 1
    Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx

    Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

    Published 2024
    “…The experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. …”
    Save to List
    Saved in:
  2. 2
    Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx

    Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx by Bettina Katalin Budai (13951317)

    Published 2022
    Save to List
    Saved in:
  3. 3
    Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX

    Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX by Bettina Katalin Budai (13951317)

    Published 2022
    Save to List
    Saved in:
  4. 4
    Data_Sheet_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.XLSX

    Data_Sheet_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.XLSX by Bettina Katalin Budai (13951317)

    Published 2022
    Save to List
    Saved in:
  5. 5
    <b>Multimodal MRI radiomics</b><b> based on </b><b>habitat subregions of the tumor microenvironment</b><b> for predicting risk stratification in glioblastoma</b>

    <b>Multimodal MRI radiomics</b><b> based on </b><b>habitat subregions of the tumor microenvironment</b><b> for predicting risk stratification in glioblastoma</b> by Han Wang (21457334)

    Published 2025
    “…</p><p dir="ltr">A fully automated approach involving label fusion from multiple deep learning algorithms was used to segment distinct tumor subregions histologically. …”
    Save to List
    Saved in:
  6. 6
    Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  7. 7
    Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  8. 8
    Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  9. 9
    Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx

    Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  10. 10
    Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  11. 11
    Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:

Search Tools: