Please enable JavaScript.

Coggle requires JavaScript to display documents.

DL for MI (Medical Image) - Coggle Diagram

- - - - Name Dataset : Chest X-ray14
      - Purpose: detection
      - Number 112.120 images for 30.805 patients
    - - Purpose : Detection
      - Name DataSet : MURA
- - - - hybrid model – in this model, the output of convolution
        layer is directly passed as input to other DL architecture
        such as residual attention network , recurrent convolutional neural
        network (RCNN) and inception recurrent residual convolutional
        neural network (IRRCNN) model (Alom et al., 2019);
      - Integrated model in this model, the output of one DL model
        is passed as input to other DL model
      - Embedded model – in this
        model, the dimension reduction model and classification model
        are jointly optimized for example enhanced joint hybrid CNNBiLSTM (EJH-CNN-BiLSTM)
      - Transfer learning (TL) –
        in this model, DL model trained on one type of problem is used
        for the same type of problem Popular CNN models which are
        used as TL models are VGG (e.g. VGG16 or VGG19), GoogLeNet (e.g. InceptionV3), Inception Network (Inception-v4), Residual neural Network (e.g. ResNet50), AlexNet.
      - Joint AB based DL combines
        two types of pooling to obtain optimal features: max pooling,
        and attentive pooling.
    - - Supervised
        
        long short-term memory (LSTM)
        
        convolutional neural networks (CNNs)
        
        CNN contains multiple layers which are arranged in a hierarchical fashion. Each layer learns specific features of the
        image (Vizcarra et al., 2019). It consists of convolutional layers,
        pooling layers, dropout layers, and an output layer.
        
        Architectures
        
        AlexNet: It consists of 5 convolution and 3 dense layers, max
        pooling, dropout, data augmentation, ReLU activations after every
        convolutional and fully-connected layer, SGD with momentum
        (Krizhevsky et al., 2017). It is used for object recognition.
        
        VGG (Visual Geometry Group): It consists of 13 convolution
        layers (in VGG16) & 16 convolution layers (in VGG19), 3 dense layers, pooling, and three ReLU units, very small receptive fields
        (Simonyan and Zisserman, 2014). It is used for large scale object recognition.
        
        GoogLeNet. It consisted of 22 layers deep CNN and 4 million
        parameters. It contains more filters per layer and stacked convolutional
        layers (Zhou et al., 2016). It used batch normalization, image
        distortions, and RMSprop.
        
        ResNet (Residual Neural Network): It contains gated units or
        gated recurrent units and has a strong similarity to recent successful
        elements applied in RNNs. It is able to train 152 layers NN (He
        et al., 2015). It has lower complexity than VGGNet
        
        UNet: It consists of three units: contraction, bottleneck, and
        expansion. The contraction section is made of many contraction
        blocks. Each block is arranged in a hierarchal fashion. In which
        the max-pooling layer is arranged after two convolution layers.
        Each block is followed by kernels, whose number is increasing in
        multiple of 2. It helps in learning the complex structures. The bottommost layer mediates between the contraction layer and the
        expansion layer. It consists of two CNN layers followed by the up
        convolution layer. It performs segmentation and classification in a single step (Nowling et al., 2019).
        
        DenseNET
        
        Tensor Deep Stack Networks (TDSN)
        
        gated recurrent unit (GRU),
        
        generative adversarial network (GAN):
        It is used to generate synthetic training data from original
        data using latent distribution (Hsieh et al., 2020). It composed of
        two networks, a generator, which deployed to generate synthetic
        data from noise and a discriminator, which differentiates the real
        and synthetic instances of data. Together these two adversarial
        networks improve the quality of generated data.
        
        Recurrent Neural Networks (RNNs)
        
        The RNN is very efficient to
        capture long term dependencies.
      - unsupervised
        
        deep belief networks (DBN)
        
        AutoEncoders (AE)
        
        Deep Transfer Network (DTN)
      - semi-supervised