Palm Fracture Detection using Convolution Neural Network

Authors

  • Dr. Harsh S Dave  MBBS, SBKS MI & RC, India
  • Dr. Vaishnavi Patel  MD, University of Perpetual Help Systems, Phillipines
  • Dr. Aash Gopalak  MBBS, SBKS MI & RC, India
  • Dr. Harsh Bhatt  MBBS, SBKS MI & RC, India
  • Dr. Sheshang Degadwala  Associate Professor, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dhairya Vyas  Managing Director, Shree Drashti Infotech LLP, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/IJSRST2182199

Keywords:

X-ray, Median Filter, Morphology Operation, OTSU Transform, CNN

Abstract

Palm fractures are due to punching heavy objects (such as a wall or a jaw). A major part of the human body is the palm. Palm provides the hand movement capability. Palm Bone breaks in the human body are fundamental. If the outcome of these fractures is someone's sting in the lips, the skin may be broken. In such instances, wound can be contaminated with bacteria from the other person's mouth and cause illnesses that can permanently impair use of the hand if they are not handled quickly. For analysis of the broken palm, the specialists use the X-ray picture. The manual crack detection is repetitive and the risk of error is high. In order to analyses the broken bone, a robotic system must therefore be developed. In this article, several methods are examined for identification, extraction and characterization. In addition, an inspection is often completed in good conditions and inconvenience. The proposed CNNs are also exploring the expectations of palm bone cracks as positive or negative.

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International Journal of Scientific Research in Science and Technology

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Published

2021-04-30

Issue

Volume 8, Issue 2, March-April-2021

Section

Research Articles

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How to Cite

[1]
Dr. Harsh S Dave, Dr. Vaishnavi Patel, Dr. Aash Gopalak, Dr. Harsh Bhatt, Dr. Sheshang Degadwala, Dhairya Vyas, " Palm Fracture Detection using Convolution Neural Network, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 8, Issue 2, pp.594-599, March-April-2021. Available at doi : https://doi.org/10.32628/IJSRST2182199