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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
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    Average 90 stars, based on 1 article reviews
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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:

    Convolutional neural network (CNN) architectures used for comparisons in our research.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors

    doi: 10.3390/s19040792

    Figure Lengend Snippet: Convolutional neural network (CNN) architectures used for comparisons in our research.

    Article Snippet: Our training and testing experiments were conducted by using the MATLAB implementation of CNN [ ] on a desktop computer with the following configuration: Intel® CoreTM i7-3770K CPU @ 3.50 GHz [ ], 16 GB DDR3 memory, and NVIDIA GeForce GTX 1070 graphics card (1920 CUDA cores, 8 GB GDDR5 memory) [ ].

    Techniques: