## 4 Discussion
\begin{tabular}{c} **P.D.D.** \\ **(N = 12)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Chilians** \\ **(N = 11)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Students** \\ **(N = 6)** \\ \end{tabular}  } \\ \hline \multirow{2}{*}{ \begin{tabular}{c} Sensitivity \\ **(95.05,0.95]** \\ \end{tabular}  } & 0.88 & 0.90 & 0.310 & 0.95 & 0.85 & 0.85 & 0.85 & 0.319 & 0.88 & 0.68 & 0.70 & 0.023 \\  & [0.95.05,0.95] & [0.72.05,0.95] & [0.75.05,0.95] & [0.85.10,0.05] & [0.68.09,0.95] & [0.80.05,0.95] & [0.80.05,0.80] &Therefore, the accuracy was assessed by applying CED [12] to simplify the information, wherein only the histological shape was selected from the herb image. The results showed that the accuracy of VGGNet16 (93.9%\(\rightarrow\)95.0%) did not vary widely, but those of ResNet50 (92.2%\(\rightarrow\)63.9%) and MobileNet (95.6%\(\rightarrow\)60.0%) showed a significant decline in Figure 4. There are several possible inferences for the decline in accuracy. First, not every deep learning model may perform well when a small number of images are used in the learning process, as the models employed in this study were proposed for the multi-classification capability of 1000 classes in ILSVRC. Second, the histological shape of the cut surface may not be a key element in the training process in deep learning. Third, image simplification can be an obstacle for deep-learning models with complex structures. Grad-CAM and Guided CAM visualize the weights in the deep-learning model as a heat map, and reveal the data necessary to determine which portion of the image is weighted in image identification [14]. The learning weights for ResNet50 were analyzed using Grad-CAM and Guided CAM. In some pictures, weight was assigned to the histological shape of the cut surface, similar to the method followed in the identification with the naked eye (Figure 5a). In some other pictures, the weight was assigned only to a partial area of the cut surface (Figure 5b). In the rest, the focus was on the entire image, including the appearance of the herb (Figure 5c). This showed that the identification method in the deep-learning model was not the same as followed by humans and the same type of herb could be estimated by weighting in different patterns. Another technique to improve the accuracy was to apply TL, which was presented in ILSVRC and is a learning technique that enhances the accuracy of the deep-learning model. It retrains the weight of a deep-learning model according to the data currently being learned. The accuracy of the three models prior to using TL were VGGNet16 93.9%, ResNet50 92.2%, and MobileNet 95.6%. By applying TL to the three deep-learning models, the accuracy improved to VGGNet16-TL 97.8%, ResNet50-TL 98.9%, and MobileNet-TL 99.4% (Figure 6a). The most accurate model was MobileNet-TL. Of the 180 images used in the test, one image had a prediction error in MobileNet-TL (Figure 6b). This image was low in quality and included several herbs, which were partially peeled, making it difficult to identify the herbs even with the naked eye (Figure 6c). Finally, we compared the identification performance of MobileNet-TL with that of a group of experts, and its sensitivity for all three herbs was found to be higher for MobileNet-TL than for the group of experts (Figure 7). In addition, unlike our expectation that there will be differences in the identification ability among the three groups, we have not found significant differences other than the ones seen in AC. The explanation for this is that even experts have had little chance to encounter AMC since the distribution of the herb has been banned in 2005, and there has not been active and constant training for the clinicians in distinguishing such toxic herbs. Therefore, other means are needed to replace human sensitivity to AMC. We present a deep-learning model that can be used for the toxic herb identification of AC, AMC and SA.