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cnn implemented in a common planning software  (MediCAD HECTEC)

 
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    MediCAD HECTEC cnn implemented in a common planning software
    Cnn Implemented In A Common Planning Software, supplied by MediCAD HECTEC, 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/cnn+implemented+in+a+common+planning+software/pm39308148-27-12-19
    Average 90 stars, based on 1 article reviews
    cnn implemented in a common planning software - by Bioz Stars, 2026-11
    90/100 stars

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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: