BELUGA WHALE LION OPTIMIZATION IN DEEP-NETS FOR HUMAN AGE ESTIMATION USING HAND X-RAY

Abstract

Bones undergo significant changes in size and shape with the growth of the child, and bone age estimation is crucial for determining the growth, genetic and endocrine disorders in children. Hand X-ray images are extensively utilized for diagnosing disorders in children. The variation in the chronological age and bone age indicates the presence of endocrine disorders, genetic problems, and growth abnormalities. Traditionally, bone age is estimated manually by inspecting the X-ray images, which is extremely time-consuming and prone to error. Further, the accuracy of the bone estimate depends on the experience of the medical practitioner, and thus it suffers from intra- and inter-observer variability. Hence, to overcome these issues, it is essential to devise automatic methods that can estimate the bone with high accuracy and in a short duration. In this work, bone age is estimated using a Deep Residual Network (DRN), whose learnable factors are adjusted using the devised Beluga Whale Lion Optimization (BWLO) algorithm. Further, the BWLO_DRN is examined for its superiority considering metrics, like accuracy, True Positive Rate (TPR), and True Negative Rate (TNR), and the corresponding values of 89.8%, 86.8%, and 90% are found to be achieved from the experimental results.