Abstract
Image recognition through the use of deep learning (DL) techniques has recently become a hot topic in many fields. Especially for bioimage informatics, DL-based image recognition has been successfully used in several applications, such as cancer and fracture detection. However, few previous studies have focused on detecting scaphoid fractures, and the related effectiveness is also not significant. Aimed at this issue, in this paper, we present a two-stage method for scaphoid fracture recognition by conducting an effectiveness analysis of numerous state-of-the-art artificial neural networks. In the first stage, the scaphoid bone is extracted from the radiograph using object detection techniques. Based on the object extracted, several convolutional neural networks (CNNs), with or without transfer learning, are utilized to recognize the segmented object. Finally, the analytical details on a real data set are given, in terms of various evaluation metrics, including sensitivity, specificity, precision, F1-score, area under the receiver operating curve (AUC), kappa, and accuracy. The experimental results reveal that the CNNs with transfer learning are more effective than those without transfer learning. Moreover, DenseNet201 and ResNet101 are found to be more promising than the other methods, on average. According to the experimental results, DenseNet201 and ResNet101 can be recommended as considerable solutions for scaphoid fracture detection within a bioimage diagnostic system.
Highlights
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
We detail how we studied numerous convolutional neural networks (CNNs) models as an important determinant of scaphoid fractureKCGMH
Over the past few years, deep learning methods have been successfully used in the field of bioimage recognition
Summary
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). 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.
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