Name
Abstract | ||
|---|---|---|
Deep learning has expanded essential role in healthcare system for monitoring and diagnosis support. Rather than trying to make any diagnostic decision, the system just acting as existing medical wisdom accessible. Therefore, this research aims to apply deep learning technique, convolutional neural networks (CNN) for bone age classification as a supporting tool for related fields in bone age diagnosis. Although there are various types of researches have been investigated for bone age assessment with CNN, the Attention mechanism has not been profoundly compared with standardized atlas collection of hand radiography for bone age assessment. Therefore, we propose the investigation of bone age prediction model based on Deep Learning techniques and Attention mechanism for screening tool in bone age evaluation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/APCCAS47518.2019.8953089 | 2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) |
Keywords | Field | DocType |
bone age,deep learning,convolutional neural network,attention mechanism | Bone age assessment,Bone age,Convolutional neural network,Computer science,Electronic engineering,Artificial intelligence,Deep learning,Healthcare system,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-2941-9 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanisa Mahayossanunt | 1 | 0 | 0.34 |
| Titichaya Thannamitsomboon | 2 | 0 | 0.34 |
| Chadaporn Keatmanee | 3 | 0 | 0.34 |