## 3 Results and Discussions
### Automatic Training  
Figure 7 shows the relation between datasets and the overall accuracy of the created model for automatic training. For the additional learning of every 5000 images, there was little increase in the overall accuracy when the additional learning started from 5 K to 20 K. There was a gradual decrease in the overall accuracy when the additional learning started from 25 K to 35 K and over 40 K dataset. There were no subsequent data when the additional learning started from 5 K to 20 K because some created models could not classify new images up to 10 classes because they had incomplete classification models. For the additional learning for every 10,000 images, there was little increase in the overall accuracy. However, when the additional learning started from 40 K, the overall accuracy was maintained at a high value. This study evaluated the overall accuracy of the additional learning and automatic classification system for CT images. From the viewpoint of additional learning, there was a significant improvement of the overall accuracy for the manual training. However, the additional dataset to be added should be prepared with a large number of images because the training for every 5000 images might be affected by specific feature amount. One of the reasons for the fluctuating accuracy, as shown in Figure 6A, might be insufficient feature information in the dataset. For the additional learning of every 5000 images, the number of images for the additional training was small perhaps because, as shown by a previous report [13], the number of CT images affected the accuracy of training the dataset. If the additional dataset included specific patients' data (for instance, the patient who suffered serious traffic  
Figure 6: Relation between datasets and overall accuracy for manual training. (**A**) Additional learning for every 5000 images, (**B**) additional learning for every 10,000 images. Figure 7: Relation between datasets and the overall accuracy for automatic training. (**A**) Additional learning for every 5000 images, (**B**) additional learning for every 10,000 images. accident), the feature amount through the training may be changed dramatically. Therefore, additional images with a variety of features should be prepared by using a high enough number of images for additional learning. On the contrary, automatic training showed no improvement in the overall accuracy, one reason being that the inherent accuracy is not affected by the created models. As the reference overall accuracy, the datasets between 5 K and 20 K were under 0.8 of the overall accuracy. Inaccurate classification affected the models created for automatic training. As a result, there was no further improvement in the overall accuracy. However, when additional learning started from the 40 K and larger datasets, the reference accuracy around 0.9 maintained the overall accuracy at this value. This means that automatic training with a model of higher inherent accuracy might be effective in performing accurate classifications. The limitations of this study are as follows. First, the hyper-parameters of the training models used are fixed parameters. Although a previous study [13] showed that the hyper-parameters and CNN architecture affected the overall accuracy, the CNN architecture of GoogLeNet is suitable for performing classification in many fields owing to its high accuracy; thus, we used fixed parameters. Second, the process of training accuracy and loss were not showed in this study because the ability to generalize was most important for the intended application [18] in the training process; thus, we only focused on the overall accuracy. However, the overfitting would hardly cause problems during training in this study because GoogLeNet adopted the inception module [19] and global average pooling [20] for preventing overfitting. Third, the additional image data was fixed at 500 images per class. Actual human CT images are often taken from a specific region such as from the lung or liver.