Development of an Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Approach | Chapter 6 | Technological Innovation in Engineering Research Vol. 7

 In this paper, an advanced automation skeletal recognition system that forecasts bone ageing from a radiograph of the left hand, wrist, and fingers is presented. A faster R-CNN uses the left-hand radiograph as input and outputs the DRU region that was discovered. Since the DRU area occupies the majority of the left-hand region, it aids in determining the bone maturity of newborns and young children and forecasts the puberty's accelerating and retarding stages. The experimental section details the setup of the 1101 radiographs of the left hand and wrist as well as the performance of the model using different optimization techniques and training sample sizes.  The recommended approach finally achieves 92 percent (radius) and 90 percent (ulna) classification accuracy after testing parameter adjustment.

Author(s) Details:

B. Sowmya Reddy,
Sreenidhi Institute of Science and Technology, Affiliated to Jawaharlal Nehru Technical University Hyderabad, Telangana, India.

Devavarapu Sreenivasarao,
Sreenidhi Institute of Science and Technology, Affiliated to Jawaharlal Nehru Technical University Hyderabad, Telangana, India.

Shaik Khasim Saheb,
Sreenidhi Institute of Science and Technology, Affiliated to Jawaharlal Nehru Technical University Hyderabad, Telangana, India.

Please see the link here: https://stm.bookpi.org/TIER-V7/article/view/7921