## 6 Conclusions and Future Works  
Over the past few years, deep learning methods have been successfully used in the field of bioimage recognition. However, few studies have focused on the detection of scaphoid fractures. In this paper, the related works are reviewed and compared comprehensively. From the review, the lack of related works inspired us to propose a two-stage method based on state-of-the-art CNNs. In the first stage, the scaphoid bone is segmented from the target image. Next, the segmentation is augmented by geometric operations. Then, the recognition model is constructed using transfer learning from pretrained CNNs. Finally, a set of CNNs were evaluated, in terms of seven metrics, and a detailed analysis was provided. In the following, the main contributions of our work are discussed. * In this paper, one of our major intentions was to narrow the recognition focus to the scaphoid bone, instead of the whole image. For this, the scaphoid bone was viewed as an object to be extracted from the target image. Therefore, the prediction cost can be reduced, and the effectiveness can be significantly increased. * Based on the segmentation, several CNNs were employed for solution of the problem. Data augmentation and transfer learning were fused to enhance the prediction quality. The experimental results revealed that the enhancement due to this fusion could tackle the overfitting problem. * For insight into the recognition results obtained for the models used, in this paper, numerous detailed evaluations were conducted. Through the overall analysis, insightful and comprehensive perspectives were obtained. Finally, based on the empirical study, the best two models could be recommended, namely DenseNet201 and ResNet101. Although we presented a set of effective enhancements for the detection of scaphoid fractures, the following issues remain to be addressed in the future:  
* First, with more data, the effectiveness can be increased. However, scaphoid fracture data are not easy to collect. For this purpose, in the future, we will attempt to gather more data from the other branches of Chang Gung Memorial Hospitals in Taiwan, including from pediatric patients, that is, the pediatric images will be added to enhance the coverage. * Second, in this paper, the adopted models operated individually. In the future, an ensemble model will be constructed by optimizing the combinations. Moreover, a progressive learning model will also be tested in order to increase the prediction quality. * Third, in addition to radiographs, CT images will be used as test data, utilizing the ideas proposed in this paper. Finally, several suggestions about future related studies with similar focus are listed:  
* Other bioimage recognition methods have also been studied by the authors' team, such as those for the detection of liver and lung tumors. It has been suggested that, regardless of the method, segmentation is useful in decreasing the computational cost and increasing the accuracy. * In real applications, a huge imbalance between positives and negatives exists, especially for cancers and noncancers. Consequently, it has been suggested that data augmentation and transfer learning are necessary. * For fracture recognition, although the experimental results indicated that most scaphoid fractures can be recognized, other symptoms, such as hydrops, joint effusion, nonunion, delayed union, avascular necrosis, arthritis, and so on also need attention, as automated recognition of those symptoms is helpful to doctors in determining the required treatments. **Author Contributions:** Conceptualization, Y.-C.T., [PERSON]-[PERSON], [PERSON]-[PERSON], C.-D.C. and [PERSON]-[PERSON]; data curation, Y.-C.T., [PERSON]-C.C., C.-D.C. and Y.-F.-C.; methodology, [PERSON]-H.S. and [PERSON]-[PERSON]; validation, B.-H.C.; writing--original draft, J.-H.S. ; writing--review and editing, J.-H.S. All authors have read and agreed to the published version of the manuscript.