Open AccessAn Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism
Author(s) -
B Sowmya Reddy*,
Devavarapu Sreenivasarao,
Shaik Khasim Saheb
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k2049.0981119
Subject(s) - convolutional neural network , computer science , identification (biology) , artificial intelligence , wrist , ulna , pattern recognition (psychology) , radiography , sample (material) , computer vision , medicine , radiology , anatomy , chemistry , botany , chromatography , biology
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.