## 1 Introduction  
To date, artificial intelligence (AI) has advanced technologies worldwide, successfully being applied in many fields such as industry, commerce, agriculture, smart life, medical informatics, and so on. In these fields, image recognition is widely used to simulate human vision (so-called computer vision). In traditional computer vision, an image can be described by its color, texture, and shape; thereby, the objects in an image can be recognized by machine learning methods. However, the effectiveness of such approaches is limited in incomplete feature analysis. To address such problems, deep learning (DL) has been proposed as a solution, which can be viewed as a set of neural networks based on human neural biology and which can recognize objects by conducting iterative feature filtering. In fact, DL has been shown to be effective for the diagnosis of cancer and fractures. For example, several online systems that allow the user to submit an image to detect the carcinomas have been developed. Normally, the accuracy can reach around 95%. In biomechanics, deep learning has been successfully used to detect general fractures in bioimages, including those in the axial skeleton, appendicular skeleton, skull, spine, ribs, sternum, and so on. Few previous studies have focused on scaphoid fracture detection. As shown in Figure 1, the scaphoid bone is one of the eight bones of the wrist and is the most common location for carpal fractures (i.e., about 70% of carpal fractures). Scaphoid fractures are mostly caused by the palms supporting the ground when falling, such that the wrists are excessively squeezed. Patients often have the wrist pain on the thumb side; this pain is especially intense during rotation. As the bone structure of the wrist is delicate and complex, it is not easy to discover the deformity from the appearance of the fracture, that is, if the fracture does not have much displacement, traditional X-ray examination often causes misjudgments and, thus, delay of treatment.  
Although some researchers have made attempts to address the issue above, the prediction result is not satisfactory. Therefore, in this paper, we propose a two-stage scaphoid fracture recognizer based on deep learning techniques. Overall, the contribution of this study over those in the literature can be summarized as follows:  
* To increase the precision, the proposed method identifies and segments the scaphoid bone first. Based on this segmentation, the recognition space is reduced into a specific area and the computational cost is also reduced.
* To discover the most effective recognizer, we provide a detailed analysis and conduct a set of experiments considering numerous existing well-known CNNs using data augmentation and transfer learning.
* Afterward, a comprehensive empirical study on a real data set is presented, and an insightful analysis is proposed, in order to make a near-optimal recommendation from the testing CNNs.
* Finally, the proposed method will be materialized further in the bioimage diagnostic system in the Kaohsiung Branch of Chang Gung Hospital, Taiwan.  
Basically, this paper can be viewed as an evaluation study, and the major intent behind it is to enhance the existing techniques through the use of segmentation, data augmentation, and transfer learning. By conducting an effectiveness assessment, we can confidently recommend AI networks for the desired purpose. The remainder of this paper is laid out as follows. A review of related works is provided in Section 2. In Section 3, the proposed method for scaphoid fracture detection is presented in detail. The empirical study is described in Section 4, and our conclusions are given in Section 5.  
Figure 1: Example of the scaphoid bone.