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efficientnet multiclass  (EyePACS LLC)

 
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    Structured Review

    EyePACS LLC efficientnet multiclass
    Accuracy of each architecture for each dataset for <t>multiclass</t> problem (> 90% dark, > 80% medium otherwise light).
    Efficientnet Multiclass, supplied by EyePACS LLC, 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/result/efficientnet multiclass/product/EyePACS LLC
    Average 90 stars, based on 1 article reviews
    efficientnet multiclass - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "A comparative evaluation of deep learning approaches for ophthalmology"

    Article Title: A comparative evaluation of deep learning approaches for ophthalmology

    Journal: Scientific Reports

    doi: 10.1038/s41598-024-72752-x

    Accuracy of each architecture for each dataset for multiclass problem (> 90% dark, > 80% medium otherwise light).
    Figure Legend Snippet: Accuracy of each architecture for each dataset for multiclass problem (> 90% dark, > 80% medium otherwise light).

    Techniques Used:

    Original ACRIMA glaucoma image and the heatmap generated from EfficientNet.
    Figure Legend Snippet: Original ACRIMA glaucoma image and the heatmap generated from EfficientNet.

    Techniques Used: Generated

    List of architectures and accuracy (in %) of each dataset for multiclass problem.
    Figure Legend Snippet: List of architectures and accuracy (in %) of each dataset for multiclass problem.

    Techniques Used:

    Performance metrics of grading classifiers.
    Figure Legend Snippet: Performance metrics of grading classifiers.

    Techniques Used:

    Accuracy (in %) of CNN classifiers on different 2D OCT datasets.
    Figure Legend Snippet: Accuracy (in %) of CNN classifiers on different 2D OCT datasets.

    Techniques Used:



    Similar Products

    efficientnet multiclass  (EyePACS LLC)
    90
    EyePACS LLC efficientnet multiclass
    Accuracy of each architecture for each dataset for <t>multiclass</t> problem (> 90% dark, > 80% medium otherwise light).
    Efficientnet Multiclass, supplied by EyePACS LLC, 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/result/efficientnet multiclass/product/EyePACS LLC
    Average 90 stars, based on 1 article reviews
    efficientnet multiclass - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Accuracy of each architecture for each dataset for multiclass problem (> 90% dark, > 80% medium otherwise light).

    Journal: Scientific Reports

    Article Title: A comparative evaluation of deep learning approaches for ophthalmology

    doi: 10.1038/s41598-024-72752-x

    Figure Lengend Snippet: Accuracy of each architecture for each dataset for multiclass problem (> 90% dark, > 80% medium otherwise light).

    Article Snippet: The comparison showed that there are few disadvantages to using grading over multiclass classification, especially since the accuracy is similar (85% for EfficientNet multiclass Eyepacs versus 88% for grading).

    Techniques:

    Original ACRIMA glaucoma image and the heatmap generated from EfficientNet.

    Journal: Scientific Reports

    Article Title: A comparative evaluation of deep learning approaches for ophthalmology

    doi: 10.1038/s41598-024-72752-x

    Figure Lengend Snippet: Original ACRIMA glaucoma image and the heatmap generated from EfficientNet.

    Article Snippet: The comparison showed that there are few disadvantages to using grading over multiclass classification, especially since the accuracy is similar (85% for EfficientNet multiclass Eyepacs versus 88% for grading).

    Techniques: Generated

    List of architectures and accuracy (in %) of each dataset for multiclass problem.

    Journal: Scientific Reports

    Article Title: A comparative evaluation of deep learning approaches for ophthalmology

    doi: 10.1038/s41598-024-72752-x

    Figure Lengend Snippet: List of architectures and accuracy (in %) of each dataset for multiclass problem.

    Article Snippet: The comparison showed that there are few disadvantages to using grading over multiclass classification, especially since the accuracy is similar (85% for EfficientNet multiclass Eyepacs versus 88% for grading).

    Techniques:

    Performance metrics of grading classifiers.

    Journal: Scientific Reports

    Article Title: A comparative evaluation of deep learning approaches for ophthalmology

    doi: 10.1038/s41598-024-72752-x

    Figure Lengend Snippet: Performance metrics of grading classifiers.

    Article Snippet: The comparison showed that there are few disadvantages to using grading over multiclass classification, especially since the accuracy is similar (85% for EfficientNet multiclass Eyepacs versus 88% for grading).

    Techniques:

    Accuracy (in %) of CNN classifiers on different 2D OCT datasets.

    Journal: Scientific Reports

    Article Title: A comparative evaluation of deep learning approaches for ophthalmology

    doi: 10.1038/s41598-024-72752-x

    Figure Lengend Snippet: Accuracy (in %) of CNN classifiers on different 2D OCT datasets.

    Article Snippet: The comparison showed that there are few disadvantages to using grading over multiclass classification, especially since the accuracy is similar (85% for EfficientNet multiclass Eyepacs versus 88% for grading).

    Techniques: