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An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures

Faiyaz Mohammad Saif, Jabin Rubayat, Md.Hosne Al Walid. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Faiyaz Mohammad Saif, Jabin Rubayat, Md.Hosne Al Walid

Faiyaz Mohammad Saif, Jabin Rubayat and Md.Hosne Al Walid. An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures. Communications on Applied Electronics 7(22):14-20, November 2018. BibTeX

	author = {Faiyaz Mohammad Saif and Jabin Rubayat and Md.Hosne Al Walid},
	title = {An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures},
	journal = {Communications on Applied Electronics},
	issue_date = {November 2018},
	volume = {7},
	number = {22},
	month = {Nov},
	year = {2018},
	issn = {2394-4714},
	pages = {14-20},
	numpages = {7},
	url = {},
	doi = {10.5120/cae2018652794},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Distinguishing human hand fingers bone break is an extremely basic issue in restorative. The framework proposed another approach to recognize these sorts of crack by removing highlights. For doing the general procedure among MRI (Magnetic Resonance Imaging), CT (Computed Tomography) and X-beam pictures, the proposed framework utilized X-beam pictures. At first, the framework takes information from different orthopedic foundations. Subsequent to getting the information the picture preprocessing steps have been done: right off the bat pictures have been changed over into dark, at that point sifted lastly into parallel pictures. From twofold pictures the GLCM (Gray Level Co-event Matrix), minute highlights, entropy, real pivot length, minor hub length, erraticism, introduction, arched region, zone, filled zone, equiv breadth, robustness, degree, border, mean, standard deviation, relationship coefficient, middle, fluctuation, proportion, pixel, and Euclidian separation has been removed. The element esteems are prepared by the Artificial Neural Network (ANN) where the framework used to encourage forward back proliferation systems. At that point, the yield gives two qualities where it is in typical or crack. The figure of prepared Neural Network gives the execution, preparing state, relapse of the trial which is high. The tables decided the gatherings which are changed into size and shapes and furthermore gives that the pictures are in typical or crack with precision 92.24% which is superior to other. The proposed framework can effectively distinguish the pictures of crack and typical yet can't recognize its composes. Later on, the framework will attempt to test about it.


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Hand fracture images, x-ray, GLCM, moment feature, neural network, classification.