Open AccessPalm Fracture Detection using Convolution Neural Network
Author(s) -
Harsh Dave,
Vaishnavi Patel,
Aash Gopalak,
Harsh Bhatt,
Sheshang Degadwala,
Dhairya Vyas
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst2182199
Subject(s) - palm , convolutional neural network , punching , computer science , artificial intelligence , identification (biology) , computer vision , pattern recognition (psychology) , engineering , biology , mechanical engineering , physics , botany , quantum mechanics
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.