An Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism
B Sowmya Reddy1, Devavarapu Sreenivasarao2, Shaik Khasim Saheb3
1B Sowmya Reddy is currently pursuing B.Tech Degree program in Computer Science & Engineering in Sreenidhi Institute of Science and Technology, Affiliated to Jawaharlal Nehru Technical University Hyderabad, Telangana, India, PH-8500038470.
2Devavarapu Sreenivasarao is currently working an Assistant Professor in Computer Science & Engineering Department in Sreenidhi Institute of Science and Technology and his area research includes Medical Image Processing, Machine Learning. PH-9866014581.
3Shaik Khasim Saheb is currently working as Assistant Professor in Computer Science & Engineering Department in Sreenidhi Institute of Science and Technology, and his area research includes Medical Image Processing, Machine Learning. PH-9642097865.
Manuscript received on 21 August 2019. | Revised Manuscript received on 07 September 2019. | Manuscript published on 30 September 2019. | PP: 2221-2227 | Volume-8 Issue-11, September 2019. | Retrieval Number: K20490981119/2019©BEIESP | DOI: 10.35940/ijitee.K2049.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
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