Please use this identifier to cite or link to this item:
Full metadata record
|dc.identifier.citation||Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2017, 2018, Vol., , pp.911-915||en_US|
|dc.description.abstract||The Bone Age is a fairly reliable measure of persons growth and maturation of skeleton. Bone age assessment (BAA) is a procedure that is used to predict the age of a person. The construction of a complete and fairly accurate automated bone age assessment system (ABAA) requires efficient feature extraction and classification methods. In this paper, we have presented an implementation of Bag of Features (BoF) method along with Random Forest classifier on phalanges or bones of fingers. The results have outperformed previous methods available as we have achieved a mean error of 0.58 years and 0.77 years of RMSE for bone age range of 0-18 years. Our experiments have also proved that use of gender bias improves the classification. The best performance was obtained for the ring, middle and index fingers. � 2017 IEEE.||en_US|
|dc.title||Automated bone age assessment using bag of features and random forests||en_US|
|Appears in Collections:||2. Conference Papers|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.