Utilizing InceptionV3 for Categorizing Cervical Spine Fractures and Assessing Accuracy Against a Convolutional Neural Network

Kaviya H., P. Parimala

2023

Abstract

The intent of this study is to compare the accuracy of Novel InceptionV3 and Convolutional Neural Networks in detecting cervical spine fractures from CT images. The two groups of learning models proposed in this study are the Novel InceptionV3 deep learning model and the Convolutional Neural Network (CNN). Cervical fracture is the dataset taken for the analysis which is obtained from the open source Kaggle repository with a sample size of 4200 CT images. In which 3800 images were given to train the model and 400 images to evaluate the model. With the value of G power = 0.8 with 95% confidence interval the experiment is iterated tenfold. The classification accuracy yielded by the proposed algorithm Novel InceptionV3 is 94.56% while CNN obtained an accuracy of 77.32%. The T-test (p<0.001, two tailed) shows that Novel InceptionV3 appears to have more significance than CNN. Conclusion: The study investigated the performance of two deep learning models in predicting cervical spine fractures with higher accuracy. The outcome indicates that Novel InceptionV3 is more effective in comparison with convolutional neural networks.

Paper Citation

in Harvard Style

H. K. and Parimala P. (2023). Utilizing InceptionV3 for Categorizing Cervical Spine Fractures and Assessing Accuracy Against a Convolutional Neural Network. 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 583-588. DOI: 10.5220/0012543000003739

in Bibtex Style

@conference{ai4iot23,
author={Kaviya H. and P. Parimala},
title={Utilizing InceptionV3 for Categorizing Cervical Spine Fractures and Assessing Accuracy Against a Convolutional Neural Network},
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={583-588},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012543000003739},
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 - Utilizing InceptionV3 for Categorizing Cervical Spine Fractures and Assessing Accuracy Against a Convolutional Neural Network
SN - 978-989-758-661-3
AU - H. K.
AU - Parimala P.
PY - 2023
SP - 583
EP - 588
DO - 10.5220/0012543000003739
PB - SciTePress