## 4 Discussion  
Most of the current studies have proposed advanced classification methods for single-lesion chest radiographs and achieved good results. However, many diseases have complications that can be shown on one chest radiograph. This paper commits to diagnosing all possible lesions from a single image. The performance of our image feature extractor in Table 3 was first verified by ChestX-ray14 dataset. AUC was used as the metric for two reasons. Firstly, AUC is recommended as a single number measure to over accuracy when there is a skewed sample distribution, and ChestX-ray14 is extremely imbalanced [41; 43]. Secondly, it is widely used as the only measure in many studies which proposed multi-class methods for ChestX-ray14. Our methods were compared with three methods proposed in the last three years for multi-classification problem of ChestX-ray14 dataset. The purpose of Table 3 is to verify whether the image feature extractor we trained is useful rather than showing higher AUC values. If the AUC values are very low, the training of our image feature extractor fails. If AUC values of our image feature extractor are similar to AUC values in most other research work, the image feature extractor we trained is available. It was found that the 30 AUC scores of our image feature extractors are not low and some are even higher than those of other methods listed in Table 3, which is satisfactory. Note that slightly higher AUC values do not mean that the corresponding image feature extractor contributes more to our research goal. For example, the average AUC value of \"ours-res\" is higher than \"ours-vgg\". However, the model extracting image features from \"ours-vgg\" obtains the best results, as shown in Tables 4 and 6. As the goal of our work is to solve a multi-lesion classification problem; the whole model's performance of detecting multiple lesions simultaneously was then verified under precision, recall, and F-score. The whole multi-label classification model is similar to a language model where outputs of each image are continuous lesion names, as with the simplest sentence. Therefore, precision, recall, and F-score, measurement indicators in both natural language processing and classification problems, were used. As shown in Table 4, 24 experiments were conducted to determine which feature extractor is better and weather CLA network is useful. Results on precision, recall, and F-score were also compared with two other methods. It was found that all of our methods are better than SAT. However, only when CLA network is added or image features are extracted from an appropriate layer is our method better than cxnet-m2-b. It illustrates that the quality of image features has a great influence on the results and the dependency between lesions provided by CLA network improves the model's performance. The results in Table 5 also show that the original order of the lesions is not optimal for most lesion combinations. Of the 14 lesion combinations in Table 5, only one kind of lesion combination remains the same before and after the update. CLA network method makes the relationship between the lesions in each lesion combination closer by considering association information. From the results in Table 4, it is clear that conv5 layer of Vggnet extracts more discriminative features than conv4 layer of Vggnet, and block4 layer and block3 layer of Resnet. The performances of Vggc5-CAL-CLA and Resb3-CAL-CLA are better than Vggc5-CAL and Resb4-CAL, respectively. It implies that CLA network helps CAL network get better predictions by providing possible clinical dependencies between lesions. As shown in Table 6, as the best model in our work, the performance of Vggc5-CAL-CLA was verified by categories of effusion and infiltration; atelectasis and infiltration; atelectasis and effusion; atelectasis, effusion, and infiltration; and atelectasis, consolidation, effusion, and infiltration.