• Ms.M.SINDU
• Dr.R.HEMALATHA

Bone age is an important pointer for the diagnosis and timing of different diseases and the main scope of an assessment of bone age is to determine growth and development and in addition to that used to diagnose and treat childhood diseases mainly with the use of X-ray images. The main reason for this BAA is to know how the bone grows when the child develops. Segmentation of the hand bone is mainly needed at this point to explain the properties of the hand bone in detail in medical records with x-ray images. The newly developed lightweight multi-scale U-Net convolution neural network is used with the use of x-ray images in a previous study for the betterment analysis of BAA. This approach is limited to segmentation maps in pixel forms and it has been addressed by developing a new model based on profound education that considers the field of interest within and outside the field and the dimensions of the borders during the training using K-mean clustering algorithms as a pre-segmentation constraint. The children lightweight hand bone design is based on U-Net architecture it has achieved promising segmentation results, especially for segmentation of small bones of the hand for BAA.

BAA, U-Net, K-mean clustering.

"BONE AGE ASSESSMENT USING ENHANCED K-MEANS AND CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.11-17, June 2019, Available :http://www.jetir.org/papers/JETIR1907R03.pdf

"BONE AGE ASSESSMENT USING ENHANCED K-MEANS AND CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp11-17, June 2019, Available at : http://www.jetir.org/papers/JETIR1907R03.pdf