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classifier-based implementation of existing cnn models  (SoftMax Inc)

 
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    SoftMax Inc classifier-based implementation of existing cnn models
    Performance of the developed COVID-19 detection models on the unseen dataset.
    Classifier Based Implementation Of Existing Cnn Models, supplied by SoftMax 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/cnn+implementation/classifier+based+implementation+of+existing+cnn+models/pmc08403339-272-22-17
    Average 90 stars, based on 1 article reviews
    classifier-based implementation of existing cnn models - by Bioz Stars, 2026-11
    90/100 stars

    Images

    1) Product Images from "COVID-19 detection in chest X-ray images using deep boosted hybrid learning"

    Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

    Journal: Computers in Biology and Medicine

    doi: 10.1016/j.compbiomed.2021.104816

    Performance of the developed COVID-19 detection models on the unseen dataset.
    Figure Legend Snippet: Performance of the developed COVID-19 detection models on the unseen dataset.

    Techniques Used:

    Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.
    Figure Legend Snippet: Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.

    Techniques Used: Comparison

    ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .
    Figure Legend Snippet: ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .

    Techniques Used:



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    Performance of the developed COVID-19 detection models on the unseen dataset.

    Journal: Computers in Biology and Medicine

    Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

    doi: 10.1016/j.compbiomed.2021.104816

    Figure Lengend Snippet: Performance of the developed COVID-19 detection models on the unseen dataset.

    Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

    Techniques:

    Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.

    Journal: Computers in Biology and Medicine

    Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

    doi: 10.1016/j.compbiomed.2021.104816

    Figure Lengend Snippet: Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.

    Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

    Techniques: Comparison

    ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .

    Journal: Computers in Biology and Medicine

    Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

    doi: 10.1016/j.compbiomed.2021.104816

    Figure Lengend Snippet: ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .

    Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

    Techniques: