Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions
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 super...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/10/10/781 |
| _version_ | 1797551840173228032 |
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| author | Muhammad Waqas Nadeem Hock Guan Goh Abid Ali Muzammil Hussain Muhammad Adnan Khan Vasaki a/p Ponnusamy |
| author_facet | Muhammad Waqas Nadeem Hock Guan Goh Abid Ali Muzammil Hussain Muhammad Adnan Khan Vasaki a/p Ponnusamy |
| author_sort | Muhammad Waqas Nadeem |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:51:48Z |
| format | Article |
| id | doaj.art-7b271da6f81c4259a2c65c9705740111 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T15:51:48Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-7b271da6f81c4259a2c65c97057401112023-11-20T15:58:33ZengMDPI AGDiagnostics2075-44182020-10-01101078110.3390/diagnostics10100781Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future DirectionsMuhammad Waqas Nadeem0Hock Guan Goh1Abid Ali2Muzammil Hussain3Muhammad Adnan Khan4Vasaki a/p Ponnusamy5Faculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), 31900 Kampar, Perak, MalaysiaFaculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), 31900 Kampar, Perak, MalaysiaDepartment of Computer Science, Lahore Garrison University, Lahore 54000, PakistanDepartment of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore 54000, PakistanFaculty of Information and Communication Technology (FICT), Universiti Tunku Abdul Rahman (UTAR), 31900 Kampar, Perak, MalaysiaDeep 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.https://www.mdpi.com/2075-4418/10/10/781bone agedeep learningimage processinghealth caresurveysegmentation |
| spellingShingle | Muhammad Waqas Nadeem Hock Guan Goh Abid Ali Muzammil Hussain Muhammad Adnan Khan Vasaki a/p Ponnusamy Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions Diagnostics bone age deep learning image processing health care survey segmentation |
| title | Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions |
| title_full | Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions |
| title_fullStr | Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions |
| title_full_unstemmed | Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions |
| title_short | Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions |
| title_sort | bone age assessment empowered with deep learning a survey open research challenges and future directions |
| topic | bone age deep learning image processing health care survey segmentation |
| url | https://www.mdpi.com/2075-4418/10/10/781 |
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