2.4. CNN Model Architecture

In this study, the evaluation was performed using the standard CNN model ResNet [18]. ResNet was invented by He et al. [18]. It is generally accepted that the accuracy of image discrimination is improved by deepening the network layer; conversely, if the network layer is too deep, the accuracy will decrease. To deal with this, we introduced an already developed learning method called residual learning, which has the following advantage: its batch normalization solves the gradient disappearance and makes model deterioration less likely to occur [19]. Thus, ResNet is a network that can be deepened to very deep layers of over 100 layers. This representative of the ResNet architecture has layers 18, 34, 50, 101 and 152, which were selected as the CNN model in this study.

With efficient model construction, fine-tuning the weight of existing models as initial values for additional learning is possible; therefore, all CNNs were used to transfer learning with fine-tuning employed pre-trained weights using the ImageNet database [20]. The process of deep learning classification was implemented using Python language (version.3.7.10) and the Keras (version.2.4.3) Available online: https://github.com/keras-team/keras (accessed on 19th April 2021).