Open AccessDesign of a Fracture Detection System based on Deep Program in a Convolutional Neural Network
Author(s) -
Payman Hussein Hussan,
Syefy Mohammed Mangj Al-Razoky,
Hasanain Mohammed Manji Al-Rzoky
Publication year - 2021
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v18i2/web18336
Subject(s) - convolutional neural network , computer science , artificial intelligence , genetic programming , artificial neural network , test set , set (abstract data type) , data set , test data , deep learning , machine learning , noise (video) , pattern recognition (psychology) , image (mathematics) , software engineering , programming language
This paper presents an efficient method for finding fractures in bones. For this purpose, the pre-processing set includes increasing the quality of images, removing additional objects, removing noise and rotating images. The input images then enter the machine learning phase to detect the final fracture. At this stage, a Convolutional Neural Networks is created by Genetic Programming (GP). In this way, learning models are implemented in the form of GP programs. And evolve during the evolution of this program. Then finally the best program for classifying incoming images is selected. The data set in this work is divided into training and test friends who have nothing in common. The ratio of training data to test is equal to 80 to 20. Finally, experimental results show good results for the proposed method for bone fractures.