Detection of Bone Fractures in Upper Extremities Using XceptionNet and Comparing the Accuracy with Convolutional Neural

Trisha Y., P. Pramila

2023

Abstract

Goal of this study is to differentiate an unique XceptionNet model deep learning (DL) model to Convolutional neural networks (CNN) in order to recognize bone fracture at the upper extremities of hands with considerably higher accuracy. Materials and Methods: To enhance the accuracy metric of bone fracture recognition in the upper extremity areas of hands, deep learning techniques such as the novel XceptionNet model (N=10) and Convolutional Neural Networks (N=10) were iterated. In this work, bone fracture detection using x-rays images dataset was used which was acquired via Kaggle. The dataset, which has a total of 9463 x-ray images, is 181 MB in size. The Train and Val datasets were separated. There are 633 photos in the val dataset and 8987 images in the train dataset which were used to calculate accuracy for the two groups with an 80% of G power. Results and Discussion: The classification accuracy of the novel XceptionNet model is 88.74%, which is significantly greater than the accuracy of the CNN model, which is 72.50% for Bone fracture detection using x-rays images dataset. It is discovered that the novel XceptionNet model and convolutional neural networks differed statistically with a notable difference of p<0.001 (p<0.05) (2-tailed). Conclusion: The methodology used in this paper with two deep learning models namely novel XceptionNet and convolutional neural networks. The results reveal the usefulness of the latest methods in identifying bone fracture detection in the upper extremities and show their value for fracture prediction at an early stage.

Paper Citation

in Harvard Style

Y. T. and Pramila P. (2023). Detection of Bone Fractures in Upper Extremities Using XceptionNet and Comparing the Accuracy with Convolutional Neural . In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 360-366. DOI: 10.5220/0012772300003739

in Bibtex Style

@conference{ai4iot23,
author={Trisha Y. and P. Pramila},
title={Detection of Bone Fractures in Upper Extremities Using XceptionNet and Comparing the Accuracy with Convolutional Neural },
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={360-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012772300003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Detection of Bone Fractures in Upper Extremities Using XceptionNet and Comparing the Accuracy with Convolutional Neural
SN - 978-989-758-661-3
AU - Y. T.
AU - Pramila P.
PY - 2023
SP - 360
EP - 366
DO - 10.5220/0012772300003739
PB - SciTePress