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convolutional neural networks (cnns) implemented in  (MathWorks Inc)


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    MathWorks Inc convolutional neural networks (cnns) implemented in
    Convolutional Neural Networks (Cnns) Implemented In, supplied by MathWorks 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/10__36948_slash_ijfmr__2024__v06i05__27652-171-18-24
    Average 90 stars, based on 1 article reviews
    convolutional neural networks (cnns) implemented in - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network
    Article Snippet: .. Creed Jones Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA The article presents a CNN implementation using MATLAB to determine the presence of glaucoma using labeled retinal images. ..

    Generated:

    Article Title: Neonatal Seizure Detection Using Deep Convolutional Neural Networks
    Article Snippet: .. The CNN implementation was performed by the MATLAB toolbox MatConvNet.60 For training the CNN, the weights of the Conv layers were initiated with normally distributed random numbers generated by N(0, σ2), where the standard deviation, σ, equals 0.2 and 0.1 for layers 1–18 and 19– 23, respectively. ..

    Standard Deviation:

    Article Title: Neonatal Seizure Detection Using Deep Convolutional Neural Networks
    Article Snippet: .. The CNN implementation was performed by the MATLAB toolbox MatConvNet.60 For training the CNN, the weights of the Conv layers were initiated with normally distributed random numbers generated by N(0, σ2), where the standard deviation, σ, equals 0.2 and 0.1 for layers 1–18 and 19– 23, respectively. ..



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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: