Abstract
Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well.
Highlights
The advancement in medical technologies gives more effective and efficient e-health care systems to the medical industry to facilitate the clinical experts for better treatments for patients
The development of deep-learning models and applications toward bone age assessment encouraged us to present an extensive survey on different categories of BAA such as segmentation, classification, and prediction, from applications and methodology-driven perspectives
The objectives of the presented survey are (a) to present recent deep learning development for bone age assessment, (b) an overview of open research challenges for successful deep-learning models related to BAA, and (c) to highlight the successful and effective Deep learning (DL) contribution for BAA
Summary
The advancement in medical technologies gives more effective and efficient e-health care systems to the medical industry to facilitate the clinical experts for better treatments for patients. DL-based models and architectures are prominent in bone age segmentation, prediction, and classification It has performed effectively in the field of medical image analysis, object detection and recognition [21], medical image classification [22,23,24], medical image processing and segmentation [25,26,27]. The deep learning-based models efficiently resolved the problems related to automatic segmentation, classification, and prediction by using MRI and X-ray images. Among all the deep learning models, CNN gives effective performance for image segmentation, prediction, and classification. The development of deep-learning models and applications toward bone age assessment encouraged us to present an extensive survey on different categories of BAA such as segmentation, classification, and prediction, from applications and methodology-driven perspectives. The objectives of the presented survey are (a) to present recent deep learning development for bone age assessment, (b) an overview of open research challenges for successful deep-learning models related to BAA, and (c) to highlight the successful and effective DL contribution for BAA
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