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  • br Each input was a resolution image

    2020-08-03


    Each input was a 299 299 Lycopene image with 3 di-mensions corresponding to red (R), green (G), and blue
    (B) colors digitized to a 299 299 3 tensor. The input was gradually transformed by ResNet50 through 5 stages with a total of 50 rectified linear unit activations to provide nonlinearity, which is essential for CNNs. Between stages 2 and 5, residual block structure was introduced to over-come the issues of vanishing and exploding gradients, which are notorious problems for deep CNN architectures. After 5 stages, the input was transformed to a 10 10 2048 tensor. Both height and width were greatly reduced or summarized as dimensions increased from 3 to 2048, indicating that much more information was extracted beyond the original RGB pixel information. The tensor was then flattened to a 2048-element vector based on the 2048 features extracted by ResNet50.
    The features extracted by ResNet50 in each stage are shown in Figure 3. In stage 1 the image was converted to a 150 150 64 tensor, and each dimension was visualized in a gray-scale block, with darker areas indicating higher values as the imaged was processed in deeper layers.
    When the stage goes deeper, the number of dimensions became larger. In stage 5 there were 2048 blocks with a
    10 10 size. Each dimension placed specific attention on the 2-dimensional input to allow unique visualization.
    During training we randomly divided the training data-set into a training dataset (n Z 632 images) and a valida-tion dataset (n Z 158 images) with a proportion of 8:2 to refine the model during training. To increase the size of the development dataset, we performed data augmenta-tion (Fig. 4), in which each image was rotated and flipped to expand the amount of data by 8-fold. Data augmentation is guided by expert knowledge,13 has been shown to be an effective method of improving the performance of image classification,14 and has been used in visual recognition studies for human diseases.15 Data augmentation was strictly performed only on the training dataset to improve the system’s classification performance. Testing data were not augmented. After augmentation, the development and validation datasets increased to 5056 and 1264 images, respectively.
    Before feeding the data into the CNN, data were reproc-essed to reduce their noise and allow better fit to the CNN through mean subtraction and normalization, which pro-jects each value in the tensor to a standard normal distribu-tion. Prepossessing statistics were strictly computed only on training data and then applied to the validation and test datasets to avoid potential bias.
    Zhu et al Applying a CNN-CAD system to determine invasion depth for endoscopic resection
    INPUT
    IMAGE
    Width*Height*Dimension
    CONVOLUTION
    BATCH
    STAGE 1
    NORMALIZATION
    RELU
    Activated by
    RELU 1 time
    MAX POOL
    BLOCK A
    STAGE 2
    BLOCK B x2 Activated by
    RELU 9 times
    Models
    BLOCK A
    STAGE 3
    ResNet50
    BLOCK B x3 RELU 12 times
    Activated by
    BLOCK A
    STAGE 4 trained
    Pre
    BLOCK B x5 Activated by
    BLOCK A
    STAGE 5
    BLOCK B x2 Activated by
    RELU 9 times
    AVG POOL
    FLATTEN
    Extracted Features
    FULLY
    Trained by Tumor Images
    CONNECTED
    Activated by RELU 1 time
    OUTPUT Classification:
    A Model Architecture 
    INPUT
    CONV CONV
    BN BN
    RELU
    CONV Shortcut with
    BN
    RELU (to increase
    dimensions)
    CONV
    BN
    ADD
    RELU
    OUTPUT
    B Residual Block Type A
    INPUT
    CONV
    BN
    RELU
    CONV Shortcut w/o
    BN conv & bn
    RELU (dimensions
    remain
    CONV unchanged)
    BN
    ADD
    RELU
    OUTPUT
    C Residual Block Type B Convolution
    Batch Normalization Rectified Linear Units
    Figure 2. Convolutional neural network computer-aided detection system architecture CONV, Convolution; BN, Batch Normalization; RELU, Rectified Linear Units.
    Applying a CNN-CAD system to determine invasion depth for endoscopic resection Zhu et al
    from each image by
    ResNet50
    STAGE 1
    STAGE 2
    STAGE 3
    OUTPUT
    STAGE 5
    STAGE Lycopene 4
    Figure 3. Feature extraction by ResNet50.
    Testing algorithm
    After constructing the CNN-CAD system, we used a test da-taset consisting of 203 images to evaluate the classification ac-
    curacy of the system. Receiver operating characteristic curves were plotted by a set threshold. The classification given by the CNN-CAD system (P0 or P1) was compared with pulmonary circuit based on
    Zhu et al
    Applying a CNN-CAD system to determine invasion depth for endoscopic resection