Selected article for: "deep layer and neural network"

Author: Amine Amyar; Romain Modzelewski; Su Ruan
Title: Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation
  • Document date: 2020_4_21
  • ID: hiac6ur7_37
    Snippet: Experiment 3: The third experiment was the comparison between our model and convolutional neural network trained to perform classification only. The CNN used is an 8 layer deep neural network with 6 convolutional layers, where each one is followed by a Maxpooling and a Dropout regularization of 25% to prevent the model from overfitting. The feature maps go from 8 to 256 by a factor of 2 between each two layers. We used 3 x 3 filter for convolutio.....
    Document: Experiment 3: The third experiment was the comparison between our model and convolutional neural network trained to perform classification only. The CNN used is an 8 layer deep neural network with 6 convolutional layers, where each one is followed by a Maxpooling and a Dropout regularization of 25% to prevent the model from overfitting. The feature maps go from 8 to 256 by a factor of 2 between each two layers. We used 3 x 3 filter for convolution and 2 x 2 for Maxpooling. Then a Flatten followed by two Dense layers with 128 neurons and 1 neuron respectively. A Dropout of 50% is also applied to the first layer to reduce and prevent overfitting. The activation function is elu for all layers except the last one which is a sigmoid to generate a probability for each class COVID vs non-COVID. The loss function is the binary cross-entropy and the metric is the accuracy, with the Adam optimizer. The CNN was optimized in order to ensure a fair comparison with our proposed model. The model was trained for 1500 epochs with an early stopping of 35, in the same condition as our model.

    Search related documents:
    Co phrase search for related documents
    • dropout regularization and neural network: 1, 2, 3
    • dropout regularization and prevent overfitting: 1
    • dropout regularization and propose model: 1
    • early stopping and loss function: 1
    • early stopping and neural network: 1
    • feature map and loss function: 1
    • feature map and neural network: 1, 2, 3, 4, 5
    • feature map and propose model: 1
    • loss function and model condition: 1
    • loss function and neural network: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
    • loss function and propose model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
    • model condition and neural network: 1, 2, 3
    • model condition and propose model: 1, 2, 3, 4, 5, 6
    • model prevent and neural network: 1
    • model prevent and prevent overfitting: 1, 2, 3
    • model prevent and propose model: 1, 2
    • neural network and prevent overfitting: 1, 2, 3
    • neural network and propose model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
    • prevent overfitting and propose model: 1