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Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images

Akshansh Mishra1 , Priyankan Datta2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-11 , Page no. 52-55, Nov-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i11.5255

Online published on Nov 30, 2019

Copyright © Akshansh Mishra, Priyankan Datta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Akshansh Mishra, Priyankan Datta, “Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.52-55, 2019.

MLA Style Citation: Akshansh Mishra, Priyankan Datta "Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images." International Journal of Computer Sciences and Engineering 7.11 (2019): 52-55.

APA Style Citation: Akshansh Mishra, Priyankan Datta, (2019). Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images. International Journal of Computer Sciences and Engineering, 7(11), 52-55.

BibTex Style Citation:
@article{Mishra_2019,
author = {Akshansh Mishra, Priyankan Datta},
title = {Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2019},
volume = {7},
Issue = {11},
month = {11},
year = {2019},
issn = {2347-2693},
pages = {52-55},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4944},
doi = {https://doi.org/10.26438/ijcse/v7i11.5255}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i11.5255}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4944
TI - Constructing the First Convolutional Neural Network for Determining Damaged Bones and Normal Bones in X-Ray Images
T2 - International Journal of Computer Sciences and Engineering
AU - Akshansh Mishra, Priyankan Datta
PY - 2019
DA - 2019/11/30
PB - IJCSE, Indore, INDIA
SP - 52-55
IS - 11
VL - 7
SN - 2347-2693
ER -

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Abstract

Deep learning technology applied to medical imaging may become the most disruptive technology radiology has seen since the advent of digital imaging. Most researchers believe that within next 15 years, deep learning based applications will take over human and not only most of the diagnosis will be performed by intelligent machines but will also help to predict disease, prescribe medicine and guide in treatment. In this case study, Convolutional Neural Network (CNN) has been constructed to determine the nature of bones i.e. whether it is broken or intact. Python is used as a basic language for coding purpose. It can be seen that after 50 epochs the validation accuracy is 96.39 %, it shows the ability of the model to generalize to new data.

Key-Words / Index Term

Convolutional Neural Network; X-Ray images, Broken bones; Intact bones

References

[1] Zhang, L., Yang, F., Zhang, Y.D. and Zhu, Y.J., 2016, September. Road crack detection using deep convolutional neural network. In 2016 IEEE international conference on image processing (ICIP) (pp. 3708-3712). IEEE.
[2] Sahiner, B., Chan, H.P., Petrick, N., Wei, D., Helvie, M.A., Adler, D.D. and Goodsitt, M.M., 1996. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE transactions on Medical Imaging, 15(5), pp.598-610.
[3] Lo, S.C., Lou, S.L., Lin, J.S., Freedman, M.T., Chien, M.V. and Mun, S.K., 1995. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging, 14(4), pp.711-718.