An Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism

This paper puts forward a proposition of automated skeletal recognition system that takes an input of left hand-wrist-fingers radiograph and give us an output of the bone age prediction. This system is more reliable, if is successful and time-saving than those laborious, fallible and time-consuming...

Full description

Saved in:
Bibliographic Details
Published in:International journal of innovative technology and exploring engineering 2019-09, Vol.8 (11), p.2221-2227
Main Authors: Reddy, B Sowmya, Sreenivasarao, Devavarapu, Saheb, Shaik Khasim
Format: Article
Language:English
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper puts forward a proposition of automated skeletal recognition system that takes an input of left hand-wrist-fingers radiograph and give us an output of the bone age prediction. This system is more reliable, if is successful and time-saving than those laborious, fallible and time-consuming manual diagnostic methods. Here, a Faster R-CNN takes the input of left-hand radiograph giving the detected DRU region from left-hand radiograph. This output is given as an input to a properly trained CNN model. The experiment section provides us with the details regarding the experiments conducted on 1101 radiographs of left hand and wrist datasets and accuracy of model when different optimization algorithms and training sample amounts were utilized. Finally, this proposed system achieves 92% (radius) and 90% (ulna) classification accuracy after the parameter optimization.
ISSN:2278-3075
2278-3075
DOI:10.35940/ijitee.K2049.0981119